MCP Server
Neotic
app.neotic.www/neotic
What this MCP does
Enables agents to create contextual in-app experiences, announcements, and triggers.
Capabilities
Tools
140
tools
cognitive.allocate_compute
Adaptive compute budgeting: select compute tier (Fast Path to Deep Deliberation) and timeout based on EVC.
Input schema
{'type': 'object', 'title': 'cognitive_allocate_computeArguments', 'required': ['task_structure'], 'properties': {'stakes': {'type': 'string', 'title': 'Stakes', 'default': 'normal'}, 'task_structure': {'type': 'object', 'title': 'Task Structure', 'additionalProperties': True}}}
Output schema
{'type': 'object', 'title': 'cognitive_allocate_computeDictOutput', 'additionalProperties': True}
cognitive_allocate_compute
Adaptive compute budgeting: select compute tier (Fast Path to Deep Deliberation) and timeout based on EVC.
Input schema
{'type': 'object', 'title': 'cognitive_allocate_computeArguments', 'required': ['task_structure'], 'properties': {'stakes': {'type': 'string', 'title': 'Stakes', 'default': 'normal'}, 'task_structure': {'type': 'object', 'title': 'Task Structure', 'additionalProperties': True}}}
Output schema
{'type': 'object', 'title': 'cognitive_allocate_computeDictOutput', 'additionalProperties': True}
cognitive.analogical_transfer
Transfer structural strategies across disparate domains via Structure-Mapping Engine (SME).
Input schema
{'type': 'object', 'title': 'cognitive_analogical_transferArguments', 'required': ['source_task_structure_id', 'target_task_structure_id'], 'properties': {'strategy_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Strategy Id', 'default': None}, 'source_task_structure_id': {'type': 'string', 'title': 'Source Task Structure Id'}, 'target_task_structure_id': {'type': 'string', 'title': 'Target Task Structure Id'}}}
Output schema
{'type': 'object', 'title': 'cognitive_analogical_transferDictOutput', 'additionalProperties': True}
cognitive_analogical_transfer
Transfer structural strategies across disparate domains via Structure-Mapping Engine (SME).
Input schema
{'type': 'object', 'title': 'cognitive_analogical_transferArguments', 'required': ['source_task_structure_id', 'target_task_structure_id'], 'properties': {'strategy_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Strategy Id', 'default': None}, 'source_task_structure_id': {'type': 'string', 'title': 'Source Task Structure Id'}, 'target_task_structure_id': {'type': 'string', 'title': 'Target Task Structure Id'}}}
Output schema
{'type': 'object', 'title': 'cognitive_analogical_transferDictOutput', 'additionalProperties': True}
cognitive.analyze_communication
Pragmatic communication: audit speech acts against Gricean maxims (Quality, Quantity, Relation, Manner) and detect deception.
Input schema
{'type': 'object', 'title': 'cognitive_analyze_communicationArguments', 'required': ['content'], 'properties': {'claims': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Claims', 'default': None}, 'content': {'type': 'string', 'title': 'Content'}, 'act_type': {'type': 'string', 'title': 'Act Type', 'default': 'assert'}, 'sender_id': {'type': 'string', 'title': 'Sender Id', 'default': 'agent_1'}, 'recipient_id': {'type': 'string', 'title': 'Recipient Id', 'default': 'all'}, 'context_goals': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'Context Goals', 'default': None}, 'speaker_beliefs': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Speaker Beliefs', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_analyze_communicationDictOutput', 'additionalProperties': True}
cognitive_analyze_communication
Pragmatic communication: audit speech acts against Gricean maxims (Quality, Quantity, Relation, Manner) and detect deception.
Input schema
{'type': 'object', 'title': 'cognitive_analyze_communicationArguments', 'required': ['content'], 'properties': {'claims': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Claims', 'default': None}, 'content': {'type': 'string', 'title': 'Content'}, 'act_type': {'type': 'string', 'title': 'Act Type', 'default': 'assert'}, 'sender_id': {'type': 'string', 'title': 'Sender Id', 'default': 'agent_1'}, 'recipient_id': {'type': 'string', 'title': 'Recipient Id', 'default': 'all'}, 'context_goals': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'Context Goals', 'default': None}, 'speaker_beliefs': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Speaker Beliefs', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_analyze_communicationDictOutput', 'additionalProperties': True}
cognitive.arbitrate_temporal_objectives
Arbitrate short vs long term payoffs using hyperbolic vs exponential discounting and Ulysses pre-commitment contracts.
Input schema
{'type': 'object', 'title': 'cognitive_arbitrate_temporal_objectivesArguments', 'required': ['short_term_option', 'long_term_option'], 'properties': {'k_hyperbolic': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'K Hyperbolic', 'default': None}, 'long_term_option': {'type': 'object', 'title': 'Long Term Option', 'additionalProperties': True}, 'gamma_exponential': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Gamma Exponential', 'default': None}, 'short_term_option': {'type': 'object', 'title': 'Short Term Option', 'additionalProperties': True}, 'audit_action_switch': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Audit Action Switch', 'default': None}, 'register_commitment': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Register Commitment', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_arbitrate_temporal_objectivesDictOutput', 'additionalProperties': True}
cognitive_arbitrate_temporal_objectives
Arbitrate short vs long term payoffs using hyperbolic vs exponential discounting and Ulysses pre-commitment contracts.
Input schema
{'type': 'object', 'title': 'cognitive_arbitrate_temporal_objectivesArguments', 'required': ['short_term_option', 'long_term_option'], 'properties': {'k_hyperbolic': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'K Hyperbolic', 'default': None}, 'long_term_option': {'type': 'object', 'title': 'Long Term Option', 'additionalProperties': True}, 'gamma_exponential': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Gamma Exponential', 'default': None}, 'short_term_option': {'type': 'object', 'title': 'Short Term Option', 'additionalProperties': True}, 'audit_action_switch': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Audit Action Switch', 'default': None}, 'register_commitment': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Register Commitment', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_arbitrate_temporal_objectivesDictOutput', 'additionalProperties': True}
cognitive.assess_competence
Epistemic boundary awareness: classify task into KNOWN, KNOWN_UNKNOWN, or UNKNOWN_UNKNOWN (OOD) and track calibration.
Input schema
{'type': 'object', 'title': 'cognitive_assess_competenceArguments', 'required': ['task_structure'], 'properties': {'actual_outcome': {'anyOf': [{'type': 'boolean'}, {'type': 'null'}], 'title': 'Actual Outcome', 'default': None}, 'task_structure': {'type': 'object', 'title': 'Task Structure', 'additionalProperties': True}}}
Output schema
{'type': 'object', 'title': 'cognitive_assess_competenceDictOutput', 'additionalProperties': True}
cognitive_assess_competence
Epistemic boundary awareness: classify task into KNOWN, KNOWN_UNKNOWN, or UNKNOWN_UNKNOWN (OOD) and track calibration.
Input schema
{'type': 'object', 'title': 'cognitive_assess_competenceArguments', 'required': ['task_structure'], 'properties': {'actual_outcome': {'anyOf': [{'type': 'boolean'}, {'type': 'null'}], 'title': 'Actual Outcome', 'default': None}, 'task_structure': {'type': 'object', 'title': 'Task Structure', 'additionalProperties': True}}}
Output schema
{'type': 'object', 'title': 'cognitive_assess_competenceDictOutput', 'additionalProperties': True}
cognitive.audit_evidence_graph
Audit the evidence graph for a task before issuing final answers.
Rejects claims such as 'optimal', 'verified', or 'feasible' when their
evidence dependencies are incomplete.
Input schema
{'type': 'object', 'title': 'cognitive_audit_evidence_graphArguments', 'required': ['task_structure_id'], 'properties': {'task_structure_id': {'type': 'string', 'title': 'Task Structure Id'}}}
Output schema
{'type': 'object', 'title': 'cognitive_audit_evidence_graphDictOutput', 'additionalProperties': True}
cognitive_audit_evidence_graph
Audit the evidence graph for a task before issuing final answers.
Rejects claims such as 'optimal', 'verified', or 'feasible' when their
evidence dependencies are incomplete.
Input schema
{'type': 'object', 'title': 'cognitive_audit_evidence_graphArguments', 'required': ['task_structure_id'], 'properties': {'task_structure_id': {'type': 'string', 'title': 'Task Structure Id'}}}
Output schema
{'type': 'object', 'title': 'cognitive_audit_evidence_graphDictOutput', 'additionalProperties': True}
cognitive.build_evidence_graph
Build or update an Evidence-Carrying Cognitive Graph for a task structure and solution trace.
Input schema
{'type': 'object', 'title': 'cognitive_build_evidence_graphArguments', 'required': ['task_structure_id'], 'properties': {'claims': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Claims', 'default': None}, 'solution_trace': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Solution Trace', 'default': None}, 'task_structure_id': {'type': 'string', 'title': 'Task Structure Id'}}}
Output schema
{'type': 'object', 'title': 'cognitive_build_evidence_graphDictOutput', 'additionalProperties': True}
cognitive_build_evidence_graph
Build or update an Evidence-Carrying Cognitive Graph for a task structure and solution trace.
Input schema
{'type': 'object', 'title': 'cognitive_build_evidence_graphArguments', 'required': ['task_structure_id'], 'properties': {'claims': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Claims', 'default': None}, 'solution_trace': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Solution Trace', 'default': None}, 'task_structure_id': {'type': 'string', 'title': 'Task Structure Id'}}}
Output schema
{'type': 'object', 'title': 'cognitive_build_evidence_graphDictOutput', 'additionalProperties': True}
cognitive.causal_analysis
Distinguish causal effects (do-calculus) from spurious correlation via backdoor adjustment.
Input schema
{'type': 'object', 'title': 'cognitive_causal_analysisArguments', 'required': ['edges', 'treatment', 'outcome', 'observations'], 'properties': {'edges': {'type': 'array', 'items': {'type': 'array', 'items': {'type': 'string'}}, 'title': 'Edges'}, 'outcome': {'type': 'string', 'title': 'Outcome'}, 'treatment': {'type': 'string', 'title': 'Treatment'}, 'observations': {'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}, 'title': 'Observations'}}}
Output schema
{'type': 'object', 'title': 'cognitive_causal_analysisDictOutput', 'additionalProperties': True}
cognitive_causal_analysis
Distinguish causal effects (do-calculus) from spurious correlation via backdoor adjustment.
Input schema
{'type': 'object', 'title': 'cognitive_causal_analysisArguments', 'required': ['edges', 'treatment', 'outcome', 'observations'], 'properties': {'edges': {'type': 'array', 'items': {'type': 'array', 'items': {'type': 'string'}}, 'title': 'Edges'}, 'outcome': {'type': 'string', 'title': 'Outcome'}, 'treatment': {'type': 'string', 'title': 'Treatment'}, 'observations': {'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}, 'title': 'Observations'}}}
Output schema
{'type': 'object', 'title': 'cognitive_causal_analysisDictOutput', 'additionalProperties': True}
cognitive.compile_invariant_lattice
Compile a Dynamic Constraint Lattice (DCL) into algebraic boundaries, conservation laws, and reachability cones.
Input schema
{'type': 'object', 'title': 'cognitive_compile_invariant_latticeArguments', 'properties': {'task_data': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Task Data', 'default': None}, 'step_budget': {'type': 'integer', 'title': 'Step Budget', 'default': 5}, 'initial_state': {'anyOf': [{'type': 'object', 'additionalProperties': {'type': 'number'}}, {'type': 'null'}], 'title': 'Initial State', 'default': None}, 'goal_conditions': {'anyOf': [{'type': 'object', 'additionalProperties': {'type': 'array', 'items': {'anyOf': [{'type': 'number'}, {'type': 'null'}]}}}, {'type': 'null'}], 'title': 'Goal Conditions', 'default': None}, 'task_structure_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Task Structure Id', 'default': None}, 'max_rate_of_change': {'anyOf': [{'type': 'object', 'additionalProperties': {'type': 'number'}}, {'type': 'null'}], 'title': 'Max Rate Of Change', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_compile_invariant_latticeDictOutput', 'additionalProperties': True}
cognitive_compile_invariant_lattice
Compile a Dynamic Constraint Lattice (DCL) into algebraic boundaries, conservation laws, and reachability cones.
Input schema
{'type': 'object', 'title': 'cognitive_compile_invariant_latticeArguments', 'properties': {'task_data': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Task Data', 'default': None}, 'step_budget': {'type': 'integer', 'title': 'Step Budget', 'default': 5}, 'initial_state': {'anyOf': [{'type': 'object', 'additionalProperties': {'type': 'number'}}, {'type': 'null'}], 'title': 'Initial State', 'default': None}, 'goal_conditions': {'anyOf': [{'type': 'object', 'additionalProperties': {'type': 'array', 'items': {'anyOf': [{'type': 'number'}, {'type': 'null'}]}}}, {'type': 'null'}], 'title': 'Goal Conditions', 'default': None}, 'task_structure_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Task Structure Id', 'default': None}, 'max_rate_of_change': {'anyOf': [{'type': 'object', 'additionalProperties': {'type': 'number'}}, {'type': 'null'}], 'title': 'Max Rate Of Change', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_compile_invariant_latticeDictOutput', 'additionalProperties': True}
cognitive.compose_strategies
Skill Composition: synthesize a composite multi-stage StrategyIR from primitive strategies.
Chains multiple specialized skills (e.g. Graph Coloring + Topological Sort + Allocation)
into a compound pipeline with explicit stage transitions and end-to-end verification.
Input schema
{'type': 'object', 'title': 'cognitive_compose_strategiesArguments', 'required': ['strategy_ids', 'composite_name'], 'properties': {'description': {'type': 'string', 'title': 'Description', 'default': ''}, 'strategy_ids': {'type': 'array', 'items': {'type': 'string'}, 'title': 'Strategy Ids'}, 'composite_name': {'type': 'string', 'title': 'Composite Name'}}}
Output schema
{'type': 'object', 'title': 'cognitive_compose_strategiesDictOutput', 'additionalProperties': True}
cognitive_compose_strategies
Skill Composition: synthesize a composite multi-stage StrategyIR from primitive strategies.
Chains multiple specialized skills (e.g. Graph Coloring + Topological Sort + Allocation)
into a compound pipeline with explicit stage transitions and end-to-end verification.
Input schema
{'type': 'object', 'title': 'cognitive_compose_strategiesArguments', 'required': ['strategy_ids', 'composite_name'], 'properties': {'description': {'type': 'string', 'title': 'Description', 'default': ''}, 'strategy_ids': {'type': 'array', 'items': {'type': 'string'}, 'title': 'Strategy Ids'}, 'composite_name': {'type': 'string', 'title': 'Composite Name'}}}
Output schema
{'type': 'object', 'title': 'cognitive_compose_strategiesDictOutput', 'additionalProperties': True}
cognitive.compute_intrinsic_rewards
Compute intrinsic drives: novelty & prediction surprise curiosity, empowerment (channel capacity), and learning progress.
Input schema
{'type': 'object', 'title': 'cognitive_compute_intrinsic_rewardsArguments', 'required': ['actual_state'], 'properties': {'skill_name': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Skill Name', 'default': None}, 'actual_state': {'type': 'object', 'title': 'Actual State', 'additionalProperties': True}, 'skill_success': {'anyOf': [{'type': 'boolean'}, {'type': 'null'}], 'title': 'Skill Success', 'default': None}, 'predicted_state': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Predicted State', 'default': None}, 'extrinsic_reward': {'type': 'number', 'title': 'Extrinsic Reward', 'default': 0.0}, 'reachable_states': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Reachable States', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_compute_intrinsic_rewardsDictOutput', 'additionalProperties': True}
cognitive_compute_intrinsic_rewards
Compute intrinsic drives: novelty & prediction surprise curiosity, empowerment (channel capacity), and learning progress.
Input schema
{'type': 'object', 'title': 'cognitive_compute_intrinsic_rewardsArguments', 'required': ['actual_state'], 'properties': {'skill_name': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Skill Name', 'default': None}, 'actual_state': {'type': 'object', 'title': 'Actual State', 'additionalProperties': True}, 'skill_success': {'anyOf': [{'type': 'boolean'}, {'type': 'null'}], 'title': 'Skill Success', 'default': None}, 'predicted_state': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Predicted State', 'default': None}, 'extrinsic_reward': {'type': 'number', 'title': 'Extrinsic Reward', 'default': 0.0}, 'reachable_states': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Reachable States', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_compute_intrinsic_rewardsDictOutput', 'additionalProperties': True}
cognitive.compute_lattice_signature
Compute coordinate-free topological invariant signature of a lattice or task.
Input schema
{'type': 'object', 'title': 'cognitive_compute_lattice_signatureArguments', 'properties': {'task_data': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Task Data', 'default': None}, 'lattice_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Lattice Id', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_compute_lattice_signatureDictOutput', 'additionalProperties': True}
cognitive_compute_lattice_signature
Compute coordinate-free topological invariant signature of a lattice or task.
Input schema
{'type': 'object', 'title': 'cognitive_compute_lattice_signatureArguments', 'properties': {'task_data': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Task Data', 'default': None}, 'lattice_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Lattice Id', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_compute_lattice_signatureDictOutput', 'additionalProperties': True}
cognitive.compute_number_theory
Number theory: primality, factoring, extended GCD, Diophantine, modular inverse, CRT, combinatorics, Fibonacci.
Input schema
{'type': 'object', 'title': 'cognitive_compute_number_theoryArguments', 'required': ['operation'], 'properties': {'a': {'anyOf': [{'type': 'integer'}, {'type': 'null'}], 'title': 'A', 'default': None}, 'b': {'anyOf': [{'type': 'integer'}, {'type': 'null'}], 'title': 'B', 'default': None}, 'c': {'anyOf': [{'type': 'integer'}, {'type': 'null'}], 'title': 'C', 'default': None}, 'k': {'anyOf': [{'type': 'integer'}, {'type': 'null'}], 'title': 'K', 'default': None}, 'm': {'anyOf': [{'type': 'integer'}, {'type': 'null'}], 'title': 'M', 'default': None}, 'n': {'anyOf': [{'type': 'integer'}, {'type': 'null'}], 'title': 'N', 'default': None}, 'moduli': {'anyOf': [{'type': 'array', 'items': {'type': 'integer'}}, {'type': 'null'}], 'title': 'Moduli', 'default': None}, 'operation': {'type': 'string', 'title': 'Operation'}, 'remainders': {'anyOf': [{'type': 'array', 'items': {'type': 'integer'}}, {'type': 'null'}], 'title': 'Remainders', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_compute_number_theoryDictOutput', 'additionalProperties': True}
cognitive_compute_number_theory
Number theory: primality, factoring, extended GCD, Diophantine, modular inverse, CRT, combinatorics, Fibonacci.
Input schema
{'type': 'object', 'title': 'cognitive_compute_number_theoryArguments', 'required': ['operation'], 'properties': {'a': {'anyOf': [{'type': 'integer'}, {'type': 'null'}], 'title': 'A', 'default': None}, 'b': {'anyOf': [{'type': 'integer'}, {'type': 'null'}], 'title': 'B', 'default': None}, 'c': {'anyOf': [{'type': 'integer'}, {'type': 'null'}], 'title': 'C', 'default': None}, 'k': {'anyOf': [{'type': 'integer'}, {'type': 'null'}], 'title': 'K', 'default': None}, 'm': {'anyOf': [{'type': 'integer'}, {'type': 'null'}], 'title': 'M', 'default': None}, 'n': {'anyOf': [{'type': 'integer'}, {'type': 'null'}], 'title': 'N', 'default': None}, 'moduli': {'anyOf': [{'type': 'array', 'items': {'type': 'integer'}}, {'type': 'null'}], 'title': 'Moduli', 'default': None}, 'operation': {'type': 'string', 'title': 'Operation'}, 'remainders': {'anyOf': [{'type': 'array', 'items': {'type': 'integer'}}, {'type': 'null'}], 'title': 'Remainders', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_compute_number_theoryDictOutput', 'additionalProperties': True}
cognitive.counterfactual_what_if
Counterfactual engine: evaluate 'What if I had done X instead of Y at step t?' using Pearl's abduction-intervention-prediction.
Input schema
{'type': 'object', 'title': 'cognitive_counterfactual_what_ifArguments', 'required': ['factual_trace', 'intervention_step', 'counterfactual_action'], 'properties': {'dt': {'type': 'number', 'title': 'Dt', 'default': 1.0}, 'factual_trace': {'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}, 'title': 'Factual Trace'}, 'intervention_step': {'type': 'integer', 'title': 'Intervention Step'}, 'counterfactual_action': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'string'}], 'title': 'Counterfactual Action'}}}
Output schema
{'type': 'object', 'title': 'cognitive_counterfactual_what_ifDictOutput', 'additionalProperties': True}
cognitive_counterfactual_what_if
Counterfactual engine: evaluate 'What if I had done X instead of Y at step t?' using Pearl's abduction-intervention-prediction.
Input schema
{'type': 'object', 'title': 'cognitive_counterfactual_what_ifArguments', 'required': ['factual_trace', 'intervention_step', 'counterfactual_action'], 'properties': {'dt': {'type': 'number', 'title': 'Dt', 'default': 1.0}, 'factual_trace': {'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}, 'title': 'Factual Trace'}, 'intervention_step': {'type': 'integer', 'title': 'Intervention Step'}, 'counterfactual_action': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'string'}], 'title': 'Counterfactual Action'}}}
Output schema
{'type': 'object', 'title': 'cognitive_counterfactual_what_ifDictOutput', 'additionalProperties': True}
cognitive.create_simulated_environment
Instantiate and initialize a simulated cognitive environment (spatial_commons, multi_agent_arena, sequential_puzzle).
Input schema
{'type': 'object', 'title': 'cognitive_create_simulated_environmentArguments', 'properties': {'env_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Env Id', 'default': None}, 'env_type': {'type': 'string', 'title': 'Env Type', 'default': 'spatial_commons'}, 'parameters': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Parameters', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_create_simulated_environmentDictOutput', 'additionalProperties': True}
cognitive_create_simulated_environment
Instantiate and initialize a simulated cognitive environment (spatial_commons, multi_agent_arena, sequential_puzzle).
Input schema
{'type': 'object', 'title': 'cognitive_create_simulated_environmentArguments', 'properties': {'env_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Env Id', 'default': None}, 'env_type': {'type': 'string', 'title': 'Env Type', 'default': 'spatial_commons'}, 'parameters': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Parameters', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_create_simulated_environmentDictOutput', 'additionalProperties': True}
cognitive.crucible_stress_test
Subject candidate trajectories to adversarial algebraic perturbations to calculate Robustness Index (R) and project hardened paths.
Input schema
{'type': 'object', 'title': 'cognitive_crucible_stress_testArguments', 'properties': {'lattice_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Lattice Id', 'default': None}, 'trajectory': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Trajectory', 'default': None}, 'step_budget': {'type': 'integer', 'title': 'Step Budget', 'default': 5}, 'lattice_data': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Lattice Data', 'default': None}, 'stress_amplitude': {'type': 'number', 'title': 'Stress Amplitude', 'default': 0.15}, 'max_perturbations': {'type': 'integer', 'title': 'Max Perturbations', 'default': 24}}}
Output schema
{'type': 'object', 'title': 'cognitive_crucible_stress_testDictOutput', 'additionalProperties': True}
cognitive_crucible_stress_test
Subject candidate trajectories to adversarial algebraic perturbations to calculate Robustness Index (R) and project hardened paths.
Input schema
{'type': 'object', 'title': 'cognitive_crucible_stress_testArguments', 'properties': {'lattice_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Lattice Id', 'default': None}, 'trajectory': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Trajectory', 'default': None}, 'step_budget': {'type': 'integer', 'title': 'Step Budget', 'default': 5}, 'lattice_data': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Lattice Data', 'default': None}, 'stress_amplitude': {'type': 'number', 'title': 'Stress Amplitude', 'default': 0.15}, 'max_perturbations': {'type': 'integer', 'title': 'Max Perturbations', 'default': 24}}}
Output schema
{'type': 'object', 'title': 'cognitive_crucible_stress_testDictOutput', 'additionalProperties': True}
cognitive.evaluate_claim_evidence
Evaluate support status and confidence for an individual claim with evidence.
Input schema
{'type': 'object', 'title': 'cognitive_evaluate_claim_evidenceArguments', 'required': ['claim'], 'properties': {'claim': {'type': 'string', 'title': 'Claim'}, 'object': {'type': 'string', 'title': 'Object', 'default': ''}, 'subject': {'type': 'string', 'title': 'Subject', 'default': ''}, 'evidence': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Evidence', 'default': None}, 'relation': {'type': 'string', 'title': 'Relation', 'default': 'states'}, 'assumptions': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'Assumptions', 'default': None}, 'dependencies': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'Dependencies', 'default': None}, 'task_structure_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Task Structure Id', 'default': None}, 'invalidation_conditions': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'Invalidation Conditions', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_evaluate_claim_evidenceDictOutput', 'additionalProperties': True}
cognitive_evaluate_claim_evidence
Evaluate support status and confidence for an individual claim with evidence.
Input schema
{'type': 'object', 'title': 'cognitive_evaluate_claim_evidenceArguments', 'required': ['claim'], 'properties': {'claim': {'type': 'string', 'title': 'Claim'}, 'object': {'type': 'string', 'title': 'Object', 'default': ''}, 'subject': {'type': 'string', 'title': 'Subject', 'default': ''}, 'evidence': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Evidence', 'default': None}, 'relation': {'type': 'string', 'title': 'Relation', 'default': 'states'}, 'assumptions': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'Assumptions', 'default': None}, 'dependencies': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'Dependencies', 'default': None}, 'task_structure_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Task Structure Id', 'default': None}, 'invalidation_conditions': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'Invalidation Conditions', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_evaluate_claim_evidenceDictOutput', 'additionalProperties': True}
cognitive.evaluate_cooperation
Multi-agent cooperation: analyze game payoff matrices, compute Nash/Pareto equilibria, and execute reciprocity policies.
Input schema
{'type': 'object', 'title': 'cognitive_evaluate_cooperationArguments', 'required': ['game_type'], 'properties': {'strategy': {'type': 'string', 'title': 'Strategy', 'default': 'tit_for_tat'}, 'game_type': {'type': 'string', 'title': 'Game Type'}, 'endowments': {'anyOf': [{'type': 'object', 'additionalProperties': {'type': 'number'}}, {'type': 'null'}], 'title': 'Endowments', 'default': None}, 'multiplier': {'type': 'number', 'title': 'Multiplier', 'default': 1.6}, 'my_history': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'My History', 'default': None}, 'contributions': {'anyOf': [{'type': 'object', 'additionalProperties': {'type': 'number'}}, {'type': 'null'}], 'title': 'Contributions', 'default': None}, 'partner_history': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'Partner History', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_evaluate_cooperationDictOutput', 'additionalProperties': True}
cognitive_evaluate_cooperation
Multi-agent cooperation: analyze game payoff matrices, compute Nash/Pareto equilibria, and execute reciprocity policies.
Input schema
{'type': 'object', 'title': 'cognitive_evaluate_cooperationArguments', 'required': ['game_type'], 'properties': {'strategy': {'type': 'string', 'title': 'Strategy', 'default': 'tit_for_tat'}, 'game_type': {'type': 'string', 'title': 'Game Type'}, 'endowments': {'anyOf': [{'type': 'object', 'additionalProperties': {'type': 'number'}}, {'type': 'null'}], 'title': 'Endowments', 'default': None}, 'multiplier': {'type': 'number', 'title': 'Multiplier', 'default': 1.6}, 'my_history': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'My History', 'default': None}, 'contributions': {'anyOf': [{'type': 'object', 'additionalProperties': {'type': 'number'}}, {'type': 'null'}], 'title': 'Contributions', 'default': None}, 'partner_history': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'Partner History', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_evaluate_cooperationDictOutput', 'additionalProperties': True}
cognitive.evaluate_counterfactual_query
Evaluate a counterfactual query on a plan ('What if capacity drops?', 'What if a route fails?', etc.).
Input schema
{'type': 'object', 'title': 'cognitive_evaluate_counterfactual_queryArguments', 'required': ['task_structure_id', 'query_type'], 'properties': {'plan_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Plan Id', 'default': None}, 'parameters': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Parameters', 'default': None}, 'query_type': {'type': 'string', 'title': 'Query Type'}, 'task_structure_id': {'type': 'string', 'title': 'Task Structure Id'}}}
Output schema
{'type': 'object', 'title': 'cognitive_evaluate_counterfactual_queryDictOutput', 'additionalProperties': True}
cognitive_evaluate_counterfactual_query
Evaluate a counterfactual query on a plan ('What if capacity drops?', 'What if a route fails?', etc.).
Input schema
{'type': 'object', 'title': 'cognitive_evaluate_counterfactual_queryArguments', 'required': ['task_structure_id', 'query_type'], 'properties': {'plan_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Plan Id', 'default': None}, 'parameters': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Parameters', 'default': None}, 'query_type': {'type': 'string', 'title': 'Query Type'}, 'task_structure_id': {'type': 'string', 'title': 'Task Structure Id'}}}
Output schema
{'type': 'object', 'title': 'cognitive_evaluate_counterfactual_queryDictOutput', 'additionalProperties': True}
cognitive.evaluate_generalization_benchmarks
Evaluate broad generalization across spatial commons, multi-agent arenas, and sequential causal puzzles.
Input schema
{'type': 'object', 'title': 'cognitive_evaluate_generalization_benchmarksArguments', 'properties': {'benchmark_filter': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Benchmark Filter', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_evaluate_generalization_benchmarksDictOutput', 'additionalProperties': True}
cognitive_evaluate_generalization_benchmarks
Evaluate broad generalization across spatial commons, multi-agent arenas, and sequential causal puzzles.
Input schema
{'type': 'object', 'title': 'cognitive_evaluate_generalization_benchmarksArguments', 'properties': {'benchmark_filter': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Benchmark Filter', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_evaluate_generalization_benchmarksDictOutput', 'additionalProperties': True}
cognitive.execute_task
One-call orchestration: identify â gate â guide â solve â verify â report.
Parameters:
- task: Dict containing:
- task_structure (or loose definition: name, entities, constraints, etc.)
- raw (optional): Domain-specific execution payload. If omitted, returns
status='guidance_only' with 'recommended_action'='supply_raw' and
an 'expected_raw_formats' object detailing valid schemas.
Supported problem types for task.raw:
* scheduling: {"workers": [{"id": "w1", "eligible_shifts": ["s1"], "max_shifts": 1}],
"shifts": [{"id": "s1", "required_workers": 1}]}
* allocation: {"consumers": [{"id": "c1", "demands": {"r1": 1}}],
"resources": [{"id": "r1", "capacity": 2}]}
* graph: {"nodes": ["A", "B"], "edges": [["A", "B"]]}
* graph_coloring: {"nodes": ["A", "B"], "edges": [["A", "B"]], "colors": ["red", "blue"]}
* shortest_path: {"nodes": ["A", "B"], "edges": [["A", "B"]], "weights": {"A->B": 1.0}, "start": "A", "target": "B"}
* math: {"math": {"question": "...", "quantities": {...}, "equations": [...], "target_variable": "x", "ground_truth": 42.0}}
* code: {"code": {"code": "def solution()...", "tests": ["assert ..."]}}
* pddl: {"pddl": {"plan": [...], "init": {...}, "goal": {...}}}
Returns a single envelope with status (completed / guidance_only /
blocked_until_clarified / no_applicable_guidance / refused_infeasible /
failed), solution, score, assumptions, failure reasons, and expected_raw_formats.
Input schema
{'type': 'object', 'title': 'cognitive_execute_taskArguments', 'properties': {'goal': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Goal', 'default': None}, 'task': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Task', 'default': None}, 'environment': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Environment', 'default': None}, 'model_family': {'type': 'string', 'title': 'Model Family', 'default': 'generic'}}}
Output schema
{'type': 'object', 'title': 'cognitive_execute_taskDictOutput', 'additionalProperties': True}
cognitive_execute_task
One-call orchestration: identify â gate â guide â solve â verify â report.
Parameters:
- task: Dict containing:
- task_structure (or loose definition: name, entities, constraints, etc.)
- raw (optional): Domain-specific execution payload. If omitted, returns
status='guidance_only' with 'recommended_action'='supply_raw' and
an 'expected_raw_formats' object detailing valid schemas.
Supported problem types for task.raw:
* scheduling: {"workers": [{"id": "w1", "eligible_shifts": ["s1"], "max_shifts": 1}],
"shifts": [{"id": "s1", "required_workers": 1}]}
* allocation: {"consumers": [{"id": "c1", "demands": {"r1": 1}}],
"resources": [{"id": "r1", "capacity": 2}]}
* graph: {"nodes": ["A", "B"], "edges": [["A", "B"]]}
* graph_coloring: {"nodes": ["A", "B"], "edges": [["A", "B"]], "colors": ["red", "blue"]}
* shortest_path: {"nodes": ["A", "B"], "edges": [["A", "B"]], "weights": {"A->B": 1.0}, "start": "A", "target": "B"}
* math: {"math": {"question": "...", "quantities": {...}, "equations": [...], "target_variable": "x", "ground_truth": 42.0}}
* code: {"code": {"code": "def solution()...", "tests": ["assert ..."]}}
* pddl: {"pddl": {"plan": [...], "init": {...}, "goal": {...}}}
Returns a single envelope with status (completed / guidance_only /
blocked_until_clarified / no_applicable_guidance / refused_infeasible /
failed), solution, score, assumptions, failure reasons, and expected_raw_formats.
Input schema
{'type': 'object', 'title': 'cognitive_execute_taskArguments', 'properties': {'goal': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Goal', 'default': None}, 'task': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Task', 'default': None}, 'environment': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Environment', 'default': None}, 'model_family': {'type': 'string', 'title': 'Model Family', 'default': 'generic'}}}
Output schema
{'type': 'object', 'title': 'cognitive_execute_taskDictOutput', 'additionalProperties': True}
cognitive.few_shot_induce
Few-shot learning: induce a generalized procedural StrategyIR from 1-3 problem traces.
Extracts structural invariants (decision ordering, invariant contracts, verification rules)
and registers an initial candidate strategy immediately without requiring large training sets.
Input schema
{'type': 'object', 'title': 'cognitive_few_shot_induceArguments', 'required': ['task_structure_id', 'solution_trace'], 'properties': {'source_model': {'type': 'string', 'title': 'Source Model', 'default': 'few_shot_learner'}, 'strategy_name': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Strategy Name', 'default': None}, 'solution_trace': {'type': 'object', 'title': 'Solution Trace', 'additionalProperties': True}, 'task_structure_id': {'type': 'string', 'title': 'Task Structure Id'}}}
Output schema
{'type': 'object', 'title': 'cognitive_few_shot_induceDictOutput', 'additionalProperties': True}
cognitive_few_shot_induce
Few-shot learning: induce a generalized procedural StrategyIR from 1-3 problem traces.
Extracts structural invariants (decision ordering, invariant contracts, verification rules)
and registers an initial candidate strategy immediately without requiring large training sets.
Input schema
{'type': 'object', 'title': 'cognitive_few_shot_induceArguments', 'required': ['task_structure_id', 'solution_trace'], 'properties': {'source_model': {'type': 'string', 'title': 'Source Model', 'default': 'few_shot_learner'}, 'strategy_name': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Strategy Name', 'default': None}, 'solution_trace': {'type': 'object', 'title': 'Solution Trace', 'additionalProperties': True}, 'task_structure_id': {'type': 'string', 'title': 'Task Structure Id'}}}
Output schema
{'type': 'object', 'title': 'cognitive_few_shot_induceDictOutput', 'additionalProperties': True}
cognitive.generate_and_prioritize_goals
Autonomous goal synthesis from world deficits, depleted reserves, and exploration frontiers with multi-criteria prioritization.
Input schema
{'type': 'object', 'title': 'cognitive_generate_and_prioritize_goalsArguments', 'properties': {'max_active': {'type': 'integer', 'title': 'Max Active', 'default': 3}, 'world_state': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'World State', 'default': None}, 'depleted_reserves': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Depleted Reserves', 'default': None}, 'goal_status_update': {'anyOf': [{'type': 'object', 'additionalProperties': {'type': 'string'}}, {'type': 'null'}], 'title': 'Goal Status Update', 'default': None}, 'unexplored_frontiers': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Unexplored Frontiers', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_generate_and_prioritize_goalsDictOutput', 'additionalProperties': True}
cognitive_generate_and_prioritize_goals
Autonomous goal synthesis from world deficits, depleted reserves, and exploration frontiers with multi-criteria prioritization.
Input schema
{'type': 'object', 'title': 'cognitive_generate_and_prioritize_goalsArguments', 'properties': {'max_active': {'type': 'integer', 'title': 'Max Active', 'default': 3}, 'world_state': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'World State', 'default': None}, 'depleted_reserves': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Depleted Reserves', 'default': None}, 'goal_status_update': {'anyOf': [{'type': 'object', 'additionalProperties': {'type': 'string'}}, {'type': 'null'}], 'title': 'Goal Status Update', 'default': None}, 'unexplored_frontiers': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Unexplored Frontiers', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_generate_and_prioritize_goalsDictOutput', 'additionalProperties': True}
cognitive.get_experiment
Retrieve details and benchmark results of an experiment (§24, §69).
Input schema
{'type': 'object', 'title': 'cognitive_get_experimentArguments', 'required': ['experiment_id'], 'properties': {'experiment_id': {'type': 'string', 'title': 'Experiment Id'}}}
Output schema
{'type': 'object', 'title': 'cognitive_get_experimentDictOutput', 'additionalProperties': True}
cognitive_get_experiment
Retrieve details and benchmark results of an experiment (§24, §69).
Input schema
{'type': 'object', 'title': 'cognitive_get_experimentArguments', 'required': ['experiment_id'], 'properties': {'experiment_id': {'type': 'string', 'title': 'Experiment Id'}}}
Output schema
{'type': 'object', 'title': 'cognitive_get_experimentDictOutput', 'additionalProperties': True}
cognitive.get_final_evidence_result
Compile a final evidence result listing supporting evidence, assumptions, missing evidence,
contradictions, unchecked dependencies, confidence, and invalidation conditions.
Input schema
{'type': 'object', 'title': 'cognitive_get_final_evidence_resultArguments', 'required': ['task_structure_id'], 'properties': {'target_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Target Id', 'default': None}, 'task_structure_id': {'type': 'string', 'title': 'Task Structure Id'}}}
Output schema
{'type': 'object', 'title': 'cognitive_get_final_evidence_resultDictOutput', 'additionalProperties': True}
cognitive_get_final_evidence_result
Compile a final evidence result listing supporting evidence, assumptions, missing evidence,
contradictions, unchecked dependencies, confidence, and invalidation conditions.
Input schema
{'type': 'object', 'title': 'cognitive_get_final_evidence_resultArguments', 'required': ['task_structure_id'], 'properties': {'target_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Target Id', 'default': None}, 'task_structure_id': {'type': 'string', 'title': 'Task Structure Id'}}}
Output schema
{'type': 'object', 'title': 'cognitive_get_final_evidence_resultDictOutput', 'additionalProperties': True}
cognitive.get_guidance
Retrieve applicable validated strategies for a task (§24, §18).
Does NOT return unverified or suspended strategies as trusted guidance.
Provides calibrated uncertainty, applicability conditions, and negative transfer warnings.
Args:
task_structure_id: UUID of the abstract task structure.
environment: Environment characteristics.
goal: Goal description and metric targets.
available_capabilities: Capabilities supported by the caller.
model_family: Model family of the consumer agent (e.g. 'claude', 'gpt', 'local').
Returns:
Ranked list of applicable strategies with procedures, conditions, and evidence.
Failures return {"error", "detail", "hint"} â never a bare exception.
Input schema
{'type': 'object', 'title': 'cognitive_get_guidanceArguments', 'required': ['task_structure_id'], 'properties': {'goal': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Goal', 'default': None}, 'environment': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Environment', 'default': None}, 'model_family': {'type': 'string', 'title': 'Model Family', 'default': 'generic'}, 'task_structure_id': {'type': 'string', 'title': 'Task Structure Id'}, 'available_capabilities': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'Available Capabilities', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_get_guidanceDictOutput', 'additionalProperties': True}
cognitive_get_guidance
Retrieve applicable validated strategies for a task (§24, §18).
Does NOT return unverified or suspended strategies as trusted guidance.
Provides calibrated uncertainty, applicability conditions, and negative transfer warnings.
Args:
task_structure_id: UUID of the abstract task structure.
environment: Environment characteristics.
goal: Goal description and metric targets.
available_capabilities: Capabilities supported by the caller.
model_family: Model family of the consumer agent (e.g. 'claude', 'gpt', 'local').
Returns:
Ranked list of applicable strategies with procedures, conditions, and evidence.
Failures return {"error", "detail", "hint"} â never a bare exception.
Input schema
{'type': 'object', 'title': 'cognitive_get_guidanceArguments', 'required': ['task_structure_id'], 'properties': {'goal': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Goal', 'default': None}, 'environment': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Environment', 'default': None}, 'model_family': {'type': 'string', 'title': 'Model Family', 'default': 'generic'}, 'task_structure_id': {'type': 'string', 'title': 'Task Structure Id'}, 'available_capabilities': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'Available Capabilities', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_get_guidanceDictOutput', 'additionalProperties': True}
cognitive.get_strategy
Retrieve a usable strategy: steps, when to use, when not, evidence summary.
Disclosure: you learn WHAT to execute, never HOW the engine induces,
verifies, or ranks knowledge (no trust signals, audit, tenants, traces).
Input schema
{'type': 'object', 'title': 'cognitive_get_strategyArguments', 'required': ['strategy_id'], 'properties': {'strategy_id': {'type': 'string', 'title': 'Strategy Id'}}}
Output schema
{'type': 'object', 'title': 'cognitive_get_strategyDictOutput', 'additionalProperties': True}
cognitive_get_strategy
Retrieve a usable strategy: steps, when to use, when not, evidence summary.
Disclosure: you learn WHAT to execute, never HOW the engine induces,
verifies, or ranks knowledge (no trust signals, audit, tenants, traces).
Input schema
{'type': 'object', 'title': 'cognitive_get_strategyArguments', 'required': ['strategy_id'], 'properties': {'strategy_id': {'type': 'string', 'title': 'Strategy Id'}}}
Output schema
{'type': 'object', 'title': 'cognitive_get_strategyDictOutput', 'additionalProperties': True}
cognitive.get_strategy_report
Evidence dossier: baseline vs assisted, distribution, CI, failures, last eval.
Input schema
{'type': 'object', 'title': 'cognitive_get_strategy_reportArguments', 'required': ['strategy_id'], 'properties': {'strategy_id': {'type': 'string', 'title': 'Strategy Id'}}}
Output schema
{'type': 'object', 'title': 'cognitive_get_strategy_reportDictOutput', 'additionalProperties': True}
cognitive_get_strategy_report
Evidence dossier: baseline vs assisted, distribution, CI, failures, last eval.
Input schema
{'type': 'object', 'title': 'cognitive_get_strategy_reportArguments', 'required': ['strategy_id'], 'properties': {'strategy_id': {'type': 'string', 'title': 'Strategy Id'}}}
Output schema
{'type': 'object', 'title': 'cognitive_get_strategy_reportDictOutput', 'additionalProperties': True}
cognitive.ground_language
Non-LLM compositional semantics: parse utterance into semantic predicates and evaluate directly against WorldState.
Input schema
{'type': 'object', 'title': 'cognitive_ground_languageArguments', 'required': ['utterance'], 'properties': {'utterance': {'type': 'string', 'title': 'Utterance'}, 'world_state': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'World State', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_ground_languageDictOutput', 'additionalProperties': True}
cognitive_ground_language
Non-LLM compositional semantics: parse utterance into semantic predicates and evaluate directly against WorldState.
Input schema
{'type': 'object', 'title': 'cognitive_ground_languageArguments', 'required': ['utterance'], 'properties': {'utterance': {'type': 'string', 'title': 'Utterance'}, 'world_state': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'World State', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_ground_languageDictOutput', 'additionalProperties': True}
cognitive.hierarchical_plan
Decompose high-level goals into milestone subgoals using Hierarchical Task Network (HTN) planning.
Input schema
{'type': 'object', 'title': 'cognitive_hierarchical_planArguments', 'required': ['goal_tasks', 'initial_state'], 'properties': {'goal_tasks': {'type': 'array', 'items': {'type': 'string'}, 'title': 'Goal Tasks'}, 'initial_state': {'type': 'object', 'title': 'Initial State', 'additionalProperties': True}, 'compound_tasks': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Compound Tasks', 'default': None}, 'primitive_operators': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Primitive Operators', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_hierarchical_planDictOutput', 'additionalProperties': True}
cognitive_hierarchical_plan
Decompose high-level goals into milestone subgoals using Hierarchical Task Network (HTN) planning.
Input schema
{'type': 'object', 'title': 'cognitive_hierarchical_planArguments', 'required': ['goal_tasks', 'initial_state'], 'properties': {'goal_tasks': {'type': 'array', 'items': {'type': 'string'}, 'title': 'Goal Tasks'}, 'initial_state': {'type': 'object', 'title': 'Initial State', 'additionalProperties': True}, 'compound_tasks': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Compound Tasks', 'default': None}, 'primitive_operators': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Primitive Operators', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_hierarchical_planDictOutput', 'additionalProperties': True}
cognitive.identify_task
Create or resolve an abstract task structure without storing raw private content (§24).
Args:
task_structure: Structural representation (entities, constraints, variables, etc.).
environment: Environmental context and characteristics.
goal: Objective and optimization goals.
Returns:
task_structure_id, structural_features, and matching existing structures.
On invalid input returns {"error", "detail", "hint"} instead of raising,
so the MCP client sees the cause instead of a generic execution error.
Input schema
{'type': 'object', 'title': 'cognitive_identify_taskArguments', 'properties': {'goal': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Goal', 'default': None}, 'environment': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Environment', 'default': None}, 'task_structure': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Task Structure', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_identify_taskDictOutput', 'additionalProperties': True}
cognitive_identify_task
Create or resolve an abstract task structure without storing raw private content (§24).
Args:
task_structure: Structural representation (entities, constraints, variables, etc.).
environment: Environmental context and characteristics.
goal: Objective and optimization goals.
Returns:
task_structure_id, structural_features, and matching existing structures.
On invalid input returns {"error", "detail", "hint"} instead of raising,
so the MCP client sees the cause instead of a generic execution error.
Input schema
{'type': 'object', 'title': 'cognitive_identify_taskArguments', 'properties': {'goal': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Goal', 'default': None}, 'environment': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Environment', 'default': None}, 'task_structure': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Task Structure', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_identify_taskDictOutput', 'additionalProperties': True}
cognitive.induce_morphic_transfer
Discover topological homomorphism between source experience and target problem, transducing solution paths.
Input schema
{'type': 'object', 'title': 'cognitive_induce_morphic_transferArguments', 'properties': {'step_budget': {'type': 'integer', 'title': 'Step Budget', 'default': 5}, 'target_task_data': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Target Task Data', 'default': None}, 'source_lattice_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Source Lattice Id', 'default': None}, 'target_lattice_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Target Lattice Id', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_induce_morphic_transferDictOutput', 'additionalProperties': True}
cognitive_induce_morphic_transfer
Discover topological homomorphism between source experience and target problem, transducing solution paths.
Input schema
{'type': 'object', 'title': 'cognitive_induce_morphic_transferArguments', 'properties': {'step_budget': {'type': 'integer', 'title': 'Step Budget', 'default': 5}, 'target_task_data': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Target Task Data', 'default': None}, 'source_lattice_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Source Lattice Id', 'default': None}, 'target_lattice_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Target Lattice Id', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_induce_morphic_transferDictOutput', 'additionalProperties': True}
cognitive.infer
Perform logical deduction (Horn clauses) or exact probabilistic Bayesian network inference.
Input schema
{'type': 'object', 'title': 'cognitive_inferArguments', 'properties': {'facts': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'Facts', 'default': None}, 'nodes': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Nodes', 'default': None}, 'query': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Query', 'default': None}, 'rules': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Rules', 'default': None}, 'evidence': {'anyOf': [{'type': 'object', 'additionalProperties': {'type': 'string'}}, {'type': 'null'}], 'title': 'Evidence', 'default': None}, 'query_var': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Query Var', 'default': None}, 'probabilistic': {'type': 'boolean', 'title': 'Probabilistic', 'default': False}}}
Output schema
{'type': 'object', 'title': 'cognitive_inferDictOutput', 'additionalProperties': True}
cognitive_infer
Perform logical deduction (Horn clauses) or exact probabilistic Bayesian network inference.
Input schema
{'type': 'object', 'title': 'cognitive_inferArguments', 'properties': {'facts': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'Facts', 'default': None}, 'nodes': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Nodes', 'default': None}, 'query': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Query', 'default': None}, 'rules': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Rules', 'default': None}, 'evidence': {'anyOf': [{'type': 'object', 'additionalProperties': {'type': 'string'}}, {'type': 'null'}], 'title': 'Evidence', 'default': None}, 'query_var': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Query Var', 'default': None}, 'probabilistic': {'type': 'boolean', 'title': 'Probabilistic', 'default': False}}}
Output schema
{'type': 'object', 'title': 'cognitive_inferDictOutput', 'additionalProperties': True}
cognitive.infer_human_values
Infer human values via Bradley-Terry IRL, detect Goodhart's law / specification gaming, and assess CIRL deference.
Input schema
{'type': 'object', 'title': 'cognitive_infer_human_valuesArguments', 'properties': {'comparisons': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Comparisons', 'default': None}, 'proxy_metric': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Proxy Metric', 'default': None}, 'action_stakes': {'type': 'string', 'title': 'Action Stakes', 'default': 'normal'}, 'detect_gaming': {'type': 'boolean', 'title': 'Detect Gaming', 'default': False}, 'action_evaluated': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Action Evaluated', 'default': None}, 'baseline_metrics': {'anyOf': [{'type': 'object', 'additionalProperties': {'type': 'number'}}, {'type': 'null'}], 'title': 'Baseline Metrics', 'default': None}, 'projected_metrics': {'anyOf': [{'type': 'object', 'additionalProperties': {'type': 'number'}}, {'type': 'null'}], 'title': 'Projected Metrics', 'default': None}, 'action_irreversible': {'type': 'boolean', 'title': 'Action Irreversible', 'default': False}}}
Output schema
{'type': 'object', 'title': 'cognitive_infer_human_valuesDictOutput', 'additionalProperties': True}
cognitive_infer_human_values
Infer human values via Bradley-Terry IRL, detect Goodhart's law / specification gaming, and assess CIRL deference.
Input schema
{'type': 'object', 'title': 'cognitive_infer_human_valuesArguments', 'properties': {'comparisons': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Comparisons', 'default': None}, 'proxy_metric': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Proxy Metric', 'default': None}, 'action_stakes': {'type': 'string', 'title': 'Action Stakes', 'default': 'normal'}, 'detect_gaming': {'type': 'boolean', 'title': 'Detect Gaming', 'default': False}, 'action_evaluated': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Action Evaluated', 'default': None}, 'baseline_metrics': {'anyOf': [{'type': 'object', 'additionalProperties': {'type': 'number'}}, {'type': 'null'}], 'title': 'Baseline Metrics', 'default': None}, 'projected_metrics': {'anyOf': [{'type': 'object', 'additionalProperties': {'type': 'number'}}, {'type': 'null'}], 'title': 'Projected Metrics', 'default': None}, 'action_irreversible': {'type': 'boolean', 'title': 'Action Irreversible', 'default': False}}}
Output schema
{'type': 'object', 'title': 'cognitive_infer_human_valuesDictOutput', 'additionalProperties': True}
cognitive.inspect_lexicon
Inspect grounded lexicon acquired via situated interaction (learned vocabulary, concept bindings, confidence).
Input schema
{'type': 'object', 'title': 'cognitive_inspect_lexiconArguments', 'properties': {}}
Output schema
{'type': 'object', 'title': 'cognitive_inspect_lexiconDictOutput', 'additionalProperties': True}
cognitive_inspect_lexicon
Inspect grounded lexicon acquired via situated interaction (learned vocabulary, concept bindings, confidence).
Input schema
{'type': 'object', 'title': 'cognitive_inspect_lexiconArguments', 'properties': {}}
Output schema
{'type': 'object', 'title': 'cognitive_inspect_lexiconDictOutput', 'additionalProperties': True}
cognitive.inspect_self_model
Engine self-model introspection: inspect capabilities, domain competence, active subsystems, and safety status.
Input schema
{'type': 'object', 'title': 'cognitive_inspect_self_modelArguments', 'properties': {}}
Output schema
{'type': 'object', 'title': 'cognitive_inspect_self_modelDictOutput', 'additionalProperties': True}
cognitive_inspect_self_model
Engine self-model introspection: inspect capabilities, domain competence, active subsystems, and safety status.
Input schema
{'type': 'object', 'title': 'cognitive_inspect_self_modelArguments', 'properties': {}}
Output schema
{'type': 'object', 'title': 'cognitive_inspect_self_modelDictOutput', 'additionalProperties': True}
cognitive.learn_from_mistake
Online Real-Time Error Reflection & Strategy Patching.
When an execution fails, analyzes root-cause constraint violations, synthesizes
new exception cases and repair procedures, verifies update against anchor regression,
and publishes the patched strategy version in real time.
Input schema
{'type': 'object', 'title': 'cognitive_learn_from_mistakeArguments', 'required': ['strategy_id', 'task_instance', 'execution_trace', 'violations'], 'properties': {'violations': {'type': 'array', 'items': {'type': 'string'}, 'title': 'Violations'}, 'strategy_id': {'type': 'string', 'title': 'Strategy Id'}, 'task_instance': {'type': 'object', 'title': 'Task Instance', 'additionalProperties': True}, 'execution_trace': {'type': 'object', 'title': 'Execution Trace', 'additionalProperties': True}}}
Output schema
{'type': 'object', 'title': 'cognitive_learn_from_mistakeDictOutput', 'additionalProperties': True}
cognitive_learn_from_mistake
Online Real-Time Error Reflection & Strategy Patching.
When an execution fails, analyzes root-cause constraint violations, synthesizes
new exception cases and repair procedures, verifies update against anchor regression,
and publishes the patched strategy version in real time.
Input schema
{'type': 'object', 'title': 'cognitive_learn_from_mistakeArguments', 'required': ['strategy_id', 'task_instance', 'execution_trace', 'violations'], 'properties': {'violations': {'type': 'array', 'items': {'type': 'string'}, 'title': 'Violations'}, 'strategy_id': {'type': 'string', 'title': 'Strategy Id'}, 'task_instance': {'type': 'object', 'title': 'Task Instance', 'additionalProperties': True}, 'execution_trace': {'type': 'object', 'title': 'Execution Trace', 'additionalProperties': True}}}
Output schema
{'type': 'object', 'title': 'cognitive_learn_from_mistakeDictOutput', 'additionalProperties': True}
cognitive.learn_language_interaction
Interactive language acquisition: learn word-concept bindings through cross-situational observation, games, and feedback.
Input schema
{'type': 'object', 'title': 'cognitive_learn_language_interactionArguments', 'required': ['interaction_type', 'utterance'], 'properties': {'utterance': {'type': 'string', 'title': 'Utterance'}, 'feedback_correct': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Feedback Correct', 'default': None}, 'interaction_type': {'type': 'string', 'title': 'Interaction Type'}, 'target_object_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Target Object Id', 'default': None}, 'candidate_objects': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Candidate Objects', 'default': None}, 'referent_features': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'Referent Features', 'default': None}, 'feedback_incorrect': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Feedback Incorrect', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_learn_language_interactionDictOutput', 'additionalProperties': True}
cognitive_learn_language_interaction
Interactive language acquisition: learn word-concept bindings through cross-situational observation, games, and feedback.
Input schema
{'type': 'object', 'title': 'cognitive_learn_language_interactionArguments', 'required': ['interaction_type', 'utterance'], 'properties': {'utterance': {'type': 'string', 'title': 'Utterance'}, 'feedback_correct': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Feedback Correct', 'default': None}, 'interaction_type': {'type': 'string', 'title': 'Interaction Type'}, 'target_object_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Target Object Id', 'default': None}, 'candidate_objects': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Candidate Objects', 'default': None}, 'referent_features': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'Referent Features', 'default': None}, 'feedback_incorrect': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Feedback Incorrect', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_learn_language_interactionDictOutput', 'additionalProperties': True}
cognitive.learn_world_model
Online world model learning: update state transition priors from empirical execution traces.
Input schema
{'type': 'object', 'title': 'cognitive_learn_world_modelArguments', 'required': ['transitions'], 'properties': {'transitions': {'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}, 'title': 'Transitions'}}}
Output schema
{'type': 'object', 'title': 'cognitive_learn_world_modelDictOutput', 'additionalProperties': True}
cognitive_learn_world_model
Online world model learning: update state transition priors from empirical execution traces.
Input schema
{'type': 'object', 'title': 'cognitive_learn_world_modelArguments', 'required': ['transitions'], 'properties': {'transitions': {'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}, 'title': 'Transitions'}}}
Output schema
{'type': 'object', 'title': 'cognitive_learn_world_modelDictOutput', 'additionalProperties': True}
cognitive.list_experiments
Discovery: list recorded benchmark experiment IDs for cognitive.get_experiment.
Input schema
{'type': 'object', 'title': 'cognitive_list_experimentsArguments', 'properties': {}}
Output schema
{'type': 'object', 'title': 'cognitive_list_experimentsDictOutput', 'additionalProperties': True}
cognitive_list_experiments
Discovery: list recorded benchmark experiment IDs for cognitive.get_experiment.
Input schema
{'type': 'object', 'title': 'cognitive_list_experimentsArguments', 'properties': {}}
Output schema
{'type': 'object', 'title': 'cognitive_list_experimentsDictOutput', 'additionalProperties': True}
cognitive.matrix_algebra
Exact rational matrix & vector algebra: multiply, determinant, inverse, transpose, trace, eigenvalues, dot/cross.
Input schema
{'type': 'object', 'title': 'cognitive_matrix_algebraArguments', 'required': ['operation'], 'properties': {'matrix_A': {'anyOf': [{'type': 'array', 'items': {'type': 'array', 'items': {'type': 'number'}}}, {'type': 'null'}], 'title': 'Matrix A', 'default': None}, 'matrix_B': {'anyOf': [{'type': 'array', 'items': {'type': 'array', 'items': {'type': 'number'}}}, {'type': 'null'}], 'title': 'Matrix B', 'default': None}, 'vector_u': {'anyOf': [{'type': 'array', 'items': {'type': 'number'}}, {'type': 'null'}], 'title': 'Vector U', 'default': None}, 'vector_v': {'anyOf': [{'type': 'array', 'items': {'type': 'number'}}, {'type': 'null'}], 'title': 'Vector V', 'default': None}, 'operation': {'type': 'string', 'title': 'Operation'}}}
Output schema
{'type': 'object', 'title': 'cognitive_matrix_algebraDictOutput', 'additionalProperties': True}
cognitive_matrix_algebra
Exact rational matrix & vector algebra: multiply, determinant, inverse, transpose, trace, eigenvalues, dot/cross.
Input schema
{'type': 'object', 'title': 'cognitive_matrix_algebraArguments', 'required': ['operation'], 'properties': {'matrix_A': {'anyOf': [{'type': 'array', 'items': {'type': 'array', 'items': {'type': 'number'}}}, {'type': 'null'}], 'title': 'Matrix A', 'default': None}, 'matrix_B': {'anyOf': [{'type': 'array', 'items': {'type': 'array', 'items': {'type': 'number'}}}, {'type': 'null'}], 'title': 'Matrix B', 'default': None}, 'vector_u': {'anyOf': [{'type': 'array', 'items': {'type': 'number'}}, {'type': 'null'}], 'title': 'Vector U', 'default': None}, 'vector_v': {'anyOf': [{'type': 'array', 'items': {'type': 'number'}}, {'type': 'null'}], 'title': 'Vector V', 'default': None}, 'operation': {'type': 'string', 'title': 'Operation'}}}
Output schema
{'type': 'object', 'title': 'cognitive_matrix_algebraDictOutput', 'additionalProperties': True}
cognitive.monitor_reasoning
Introspective reasoning critic: inspect trace in-flight to catch cycles, invariant drift, vacuous output, and stalling.
Input schema
{'type': 'object', 'title': 'cognitive_monitor_reasoningArguments', 'required': ['trace_history', 'current_step'], 'properties': {'invariants': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Invariants', 'default': None}, 'current_step': {'type': 'object', 'title': 'Current Step', 'additionalProperties': True}, 'trace_history': {'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}, 'title': 'Trace History'}}}
Output schema
{'type': 'object', 'title': 'cognitive_monitor_reasoningDictOutput', 'additionalProperties': True}
cognitive_monitor_reasoning
Introspective reasoning critic: inspect trace in-flight to catch cycles, invariant drift, vacuous output, and stalling.
Input schema
{'type': 'object', 'title': 'cognitive_monitor_reasoningArguments', 'required': ['trace_history', 'current_step'], 'properties': {'invariants': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Invariants', 'default': None}, 'current_step': {'type': 'object', 'title': 'Current Step', 'additionalProperties': True}, 'trace_history': {'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}, 'title': 'Trace History'}}}
Output schema
{'type': 'object', 'title': 'cognitive_monitor_reasoningDictOutput', 'additionalProperties': True}
cognitive.parse_task
Convert natural-language task text into CIR and task_structure dict.
Every natural-language input is normalized into CIR before reasoning.
Returns both the normalized CIR and a human-readable explanation.
Input schema
{'type': 'object', 'title': 'cognitive_parse_taskArguments', 'properties': {'text': {'type': 'string', 'title': 'Text', 'default': ''}, 'prompt': {'type': 'string', 'title': 'Prompt', 'default': ''}, 'task_text': {'type': 'string', 'title': 'Task Text', 'default': ''}, 'description': {'type': 'string', 'title': 'Description', 'default': ''}}}
Output schema
{'type': 'object', 'title': 'cognitive_parse_taskDictOutput', 'additionalProperties': True}
cognitive_parse_task
Convert natural-language task text into CIR and task_structure dict.
Every natural-language input is normalized into CIR before reasoning.
Returns both the normalized CIR and a human-readable explanation.
Input schema
{'type': 'object', 'title': 'cognitive_parse_taskArguments', 'properties': {'text': {'type': 'string', 'title': 'Text', 'default': ''}, 'prompt': {'type': 'string', 'title': 'Prompt', 'default': ''}, 'task_text': {'type': 'string', 'title': 'Task Text', 'default': ''}, 'description': {'type': 'string', 'title': 'Description', 'default': ''}}}
Output schema
{'type': 'object', 'title': 'cognitive_parse_taskDictOutput', 'additionalProperties': True}
cognitive.plan_with_counterfactuals
Synthesize the best verified plan across candidate rollouts, uncertainty tracking, and constraint pruning.
Input schema
{'type': 'object', 'title': 'cognitive_plan_with_counterfactualsArguments', 'required': ['task_structure_id'], 'properties': {'goal': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Goal', 'default': None}, 'method': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Method', 'default': None}, 'horizon': {'type': 'integer', 'title': 'Horizon', 'default': 5}, 'current_state': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Current State', 'default': None}, 'task_structure_id': {'type': 'string', 'title': 'Task Structure Id'}}}
Output schema
{'type': 'object', 'title': 'cognitive_plan_with_counterfactualsDictOutput', 'additionalProperties': True}
cognitive_plan_with_counterfactuals
Synthesize the best verified plan across candidate rollouts, uncertainty tracking, and constraint pruning.
Input schema
{'type': 'object', 'title': 'cognitive_plan_with_counterfactualsArguments', 'required': ['task_structure_id'], 'properties': {'goal': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Goal', 'default': None}, 'method': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Method', 'default': None}, 'horizon': {'type': 'integer', 'title': 'Horizon', 'default': 5}, 'current_state': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Current State', 'default': None}, 'task_structure_id': {'type': 'string', 'title': 'Task Structure Id'}}}
Output schema
{'type': 'object', 'title': 'cognitive_plan_with_counterfactualsDictOutput', 'additionalProperties': True}
cognitive.predict_world_state
Forward world model: predict future state trajectories and uncertainty bounds under actions.
Input schema
{'type': 'object', 'title': 'cognitive_predict_world_stateArguments', 'required': ['state', 'actions'], 'properties': {'dt': {'type': 'number', 'title': 'Dt', 'default': 1.0}, 'state': {'type': 'object', 'title': 'State', 'additionalProperties': True}, 'actions': {'type': 'array', 'items': {}, 'title': 'Actions'}, 'timescale': {'type': 'string', 'title': 'Timescale', 'default': 'micro'}}}
Output schema
{'type': 'object', 'title': 'cognitive_predict_world_stateDictOutput', 'additionalProperties': True}
cognitive_predict_world_state
Forward world model: predict future state trajectories and uncertainty bounds under actions.
Input schema
{'type': 'object', 'title': 'cognitive_predict_world_stateArguments', 'required': ['state', 'actions'], 'properties': {'dt': {'type': 'number', 'title': 'Dt', 'default': 1.0}, 'state': {'type': 'object', 'title': 'State', 'additionalProperties': True}, 'actions': {'type': 'array', 'items': {}, 'title': 'Actions'}, 'timescale': {'type': 'string', 'title': 'Timescale', 'default': 'micro'}}}
Output schema
{'type': 'object', 'title': 'cognitive_predict_world_stateDictOutput', 'additionalProperties': True}
cognitive.project_to_manifold
Project a candidate state or plan step onto the Singular Transition Manifold M = F(S0) â© B(Goal).
Returns the corrected state, corrective delta vector Delta S = S* - S, and boundary distance margins.
Input schema
{'type': 'object', 'title': 'cognitive_project_to_manifoldArguments', 'properties': {'state': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'State', 'default': None}, 'lattice_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Lattice Id', 'default': None}, 'lattice_data': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Lattice Data', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_project_to_manifoldDictOutput', 'additionalProperties': True}
cognitive_project_to_manifold
Project a candidate state or plan step onto the Singular Transition Manifold M = F(S0) â© B(Goal).
Returns the corrected state, corrective delta vector Delta S = S* - S, and boundary distance margins.
Input schema
{'type': 'object', 'title': 'cognitive_project_to_manifoldArguments', 'properties': {'state': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'State', 'default': None}, 'lattice_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Lattice Id', 'default': None}, 'lattice_data': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Lattice Data', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_project_to_manifoldDictOutput', 'additionalProperties': True}
cognitive.propose_strategy
Propose a candidate strategy from problem-solving experience (§24, §2).
IMPORTANT: This NEVER makes the strategy TRUSTED.
The strategy enters CANDIDATE state and requires objective verification.
Input schema
{'type': 'object', 'title': 'cognitive_propose_strategyArguments', 'required': ['name', 'description', 'procedure'], 'properties': {'name': {'type': 'string', 'title': 'Name'}, 'procedure': {'type': 'array', 'items': {'type': 'string'}, 'title': 'Procedure'}, 'exceptions': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'Exceptions', 'default': None}, 'description': {'type': 'string', 'title': 'Description'}, 'source_model': {'type': 'string', 'title': 'Source Model', 'default': 'external_model'}, 'applicability': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Applicability', 'default': None}, 'preconditions': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'Preconditions', 'default': None}, 'experience_ids': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'Experience Ids', 'default': None}, 'task_structure_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Task Structure Id', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_propose_strategyDictOutput', 'additionalProperties': True}
cognitive_propose_strategy
Propose a candidate strategy from problem-solving experience (§24, §2).
IMPORTANT: This NEVER makes the strategy TRUSTED.
The strategy enters CANDIDATE state and requires objective verification.
Input schema
{'type': 'object', 'title': 'cognitive_propose_strategyArguments', 'required': ['name', 'description', 'procedure'], 'properties': {'name': {'type': 'string', 'title': 'Name'}, 'procedure': {'type': 'array', 'items': {'type': 'string'}, 'title': 'Procedure'}, 'exceptions': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'Exceptions', 'default': None}, 'description': {'type': 'string', 'title': 'Description'}, 'source_model': {'type': 'string', 'title': 'Source Model', 'default': 'external_model'}, 'applicability': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Applicability', 'default': None}, 'preconditions': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'Preconditions', 'default': None}, 'experience_ids': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'Experience Ids', 'default': None}, 'task_structure_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Task Structure Id', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_propose_strategyDictOutput', 'additionalProperties': True}
cognitive.record_experience
Record an observable event in an ongoing experience episode (§24, §7).
Accepts structured actions, observations, and state changes.
Never sends raw unredacted private transcripts.
Input schema
{'type': 'object', 'title': 'cognitive_record_experienceArguments', 'required': ['experience_id', 'event_type', 'event_data'], 'properties': {'event_data': {'type': 'object', 'title': 'Event Data', 'additionalProperties': True}, 'event_type': {'type': 'string', 'title': 'Event Type'}, 'experience_id': {'type': 'string', 'title': 'Experience Id'}}}
Output schema
{'type': 'object', 'title': 'cognitive_record_experienceDictOutput', 'additionalProperties': True}
cognitive_record_experience
Record an observable event in an ongoing experience episode (§24, §7).
Accepts structured actions, observations, and state changes.
Never sends raw unredacted private transcripts.
Input schema
{'type': 'object', 'title': 'cognitive_record_experienceArguments', 'required': ['experience_id', 'event_type', 'event_data'], 'properties': {'event_data': {'type': 'object', 'title': 'Event Data', 'additionalProperties': True}, 'event_type': {'type': 'string', 'title': 'Event Type'}, 'experience_id': {'type': 'string', 'title': 'Experience Id'}}}
Output schema
{'type': 'object', 'title': 'cognitive_record_experienceDictOutput', 'additionalProperties': True}
cognitive.refine_lattice_from_feedback
Autonomously evolve higher-order invariants, tighten bounds, and discover cliques from execution feedback.
Input schema
{'type': 'object', 'title': 'cognitive_refine_lattice_from_feedbackArguments', 'properties': {'lattice_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Lattice Id', 'default': None}, 'default_rate': {'type': 'number', 'title': 'Default Rate', 'default': 1.0}, 'lattice_data': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Lattice Data', 'default': None}, 'feedback_traces': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Feedback Traces', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_refine_lattice_from_feedbackDictOutput', 'additionalProperties': True}
cognitive_refine_lattice_from_feedback
Autonomously evolve higher-order invariants, tighten bounds, and discover cliques from execution feedback.
Input schema
{'type': 'object', 'title': 'cognitive_refine_lattice_from_feedbackArguments', 'properties': {'lattice_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Lattice Id', 'default': None}, 'default_rate': {'type': 'number', 'title': 'Default Rate', 'default': 1.0}, 'lattice_data': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Lattice Data', 'default': None}, 'feedback_traces': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Feedback Traces', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_refine_lattice_from_feedbackDictOutput', 'additionalProperties': True}
cognitive.report_transfer
Record whether a transferred strategy helped or harmed on a novel task (§24, §19).
Input schema
{'type': 'object', 'title': 'cognitive_report_transferArguments', 'required': ['strategy_id'], 'properties': {'success': {'anyOf': [{'type': 'boolean'}, {'type': 'null'}], 'title': 'Success', 'default': None}, 'strategy_id': {'type': 'string', 'title': 'Strategy Id'}, 'model_family': {'type': 'string', 'title': 'Model Family', 'default': ''}, 'transfer_type': {'type': 'string', 'title': 'Transfer Type', 'default': 'same_structure'}, 'baseline_score': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Baseline Score', 'default': None}, 'task_structure_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Task Structure Id', 'default': None}, 'with_strategy_score': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'With Strategy Score', 'default': None}, 'baseline_performance': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Baseline Performance', 'default': None}, 'consumer_model_family': {'type': 'string', 'title': 'Consumer Model Family', 'default': ''}, 'source_task_structure_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Source Task Structure Id', 'default': None}, 'target_task_structure_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Target Task Structure Id', 'default': None}, 'performance_with_strategy': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Performance With Strategy', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_report_transferDictOutput', 'additionalProperties': True}
cognitive_report_transfer
Record whether a transferred strategy helped or harmed on a novel task (§24, §19).
Input schema
{'type': 'object', 'title': 'cognitive_report_transferArguments', 'required': ['strategy_id'], 'properties': {'success': {'anyOf': [{'type': 'boolean'}, {'type': 'null'}], 'title': 'Success', 'default': None}, 'strategy_id': {'type': 'string', 'title': 'Strategy Id'}, 'model_family': {'type': 'string', 'title': 'Model Family', 'default': ''}, 'transfer_type': {'type': 'string', 'title': 'Transfer Type', 'default': 'same_structure'}, 'baseline_score': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Baseline Score', 'default': None}, 'task_structure_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Task Structure Id', 'default': None}, 'with_strategy_score': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'With Strategy Score', 'default': None}, 'baseline_performance': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Baseline Performance', 'default': None}, 'consumer_model_family': {'type': 'string', 'title': 'Consumer Model Family', 'default': ''}, 'source_task_structure_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Source Task Structure Id', 'default': None}, 'target_task_structure_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Target Task Structure Id', 'default': None}, 'performance_with_strategy': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Performance With Strategy', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_report_transferDictOutput', 'additionalProperties': True}
cognitive.resolve_intent
Pragmatics: resolve indirect speech acts (e.g. ability questions to directives), anaphoric pronouns, and verify presuppositions.
Input schema
{'type': 'object', 'title': 'cognitive_resolve_intentArguments', 'required': ['utterance'], 'properties': {'utterance': {'type': 'string', 'title': 'Utterance'}, 'speaker_id': {'type': 'string', 'title': 'Speaker Id', 'default': 'human'}, 'world_state': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'World State', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_resolve_intentDictOutput', 'additionalProperties': True}
cognitive_resolve_intent
Pragmatics: resolve indirect speech acts (e.g. ability questions to directives), anaphoric pronouns, and verify presuppositions.
Input schema
{'type': 'object', 'title': 'cognitive_resolve_intentArguments', 'required': ['utterance'], 'properties': {'utterance': {'type': 'string', 'title': 'Utterance'}, 'speaker_id': {'type': 'string', 'title': 'Speaker Id', 'default': 'human'}, 'world_state': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'World State', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_resolve_intentDictOutput', 'additionalProperties': True}
cognitive.run_closed_loop_agent
Run the end-to-end cognitive agent closed loop (Perceive -> Model -> Decide -> Act -> Reflect -> Learn).
Input schema
{'type': 'object', 'title': 'cognitive_run_closed_loop_agentArguments', 'properties': {'env_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Env Id', 'default': None}, 'actions': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Actions', 'default': None}, 'env_type': {'type': 'string', 'title': 'Env Type', 'default': 'spatial_commons'}, 'max_steps': {'type': 'integer', 'title': 'Max Steps', 'default': 20}}}
Output schema
{'type': 'object', 'title': 'cognitive_run_closed_loop_agentDictOutput', 'additionalProperties': True}
cognitive_run_closed_loop_agent
Run the end-to-end cognitive agent closed loop (Perceive -> Model -> Decide -> Act -> Reflect -> Learn).
Input schema
{'type': 'object', 'title': 'cognitive_run_closed_loop_agentArguments', 'properties': {'env_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Env Id', 'default': None}, 'actions': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Actions', 'default': None}, 'env_type': {'type': 'string', 'title': 'Env Type', 'default': 'spatial_commons'}, 'max_steps': {'type': 'integer', 'title': 'Max Steps', 'default': 20}}}
Output schema
{'type': 'object', 'title': 'cognitive_run_closed_loop_agentDictOutput', 'additionalProperties': True}
cognitive.run_multi_agent_simulation
Simulate repeated multi-agent interaction with social dilemmas, speech acts, and reputation tracking.
Input schema
{'type': 'object', 'title': 'cognitive_run_multi_agent_simulationArguments', 'properties': {'game_type': {'type': 'string', 'title': 'Game Type', 'default': 'prisoners_dilemma'}, 'num_rounds': {'type': 'integer', 'title': 'Num Rounds', 'default': 10}, 'agent_actions': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Agent Actions', 'default': None}, 'opponent_policy': {'type': 'string', 'title': 'Opponent Policy', 'default': 'tit_for_tat'}}}
Output schema
{'type': 'object', 'title': 'cognitive_run_multi_agent_simulationDictOutput', 'additionalProperties': True}
cognitive_run_multi_agent_simulation
Simulate repeated multi-agent interaction with social dilemmas, speech acts, and reputation tracking.
Input schema
{'type': 'object', 'title': 'cognitive_run_multi_agent_simulationArguments', 'properties': {'game_type': {'type': 'string', 'title': 'Game Type', 'default': 'prisoners_dilemma'}, 'num_rounds': {'type': 'integer', 'title': 'Num Rounds', 'default': 10}, 'agent_actions': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Agent Actions', 'default': None}, 'opponent_policy': {'type': 'string', 'title': 'Opponent Policy', 'default': 'tit_for_tat'}}}
Output schema
{'type': 'object', 'title': 'cognitive_run_multi_agent_simulationDictOutput', 'additionalProperties': True}
cognitive.safe_self_improve
Safe self-improvement: propose modifications guarded by immutable verification oracles and anchor regressions.
Input schema
{'type': 'object', 'title': 'cognitive_safe_self_improveArguments', 'required': ['target_component', 'patch_name', 'proposed_changes'], 'properties': {'rollback': {'type': 'boolean', 'title': 'Rollback', 'default': False}, 'patch_name': {'type': 'string', 'title': 'Patch Name'}, 'proposed_changes': {'type': 'object', 'title': 'Proposed Changes', 'additionalProperties': True}, 'target_component': {'type': 'string', 'title': 'Target Component'}, 'rollback_snapshot_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Rollback Snapshot Id', 'default': None}, 'simulated_regression_fail': {'type': 'boolean', 'title': 'Simulated Regression Fail', 'default': False}}}
Output schema
{'type': 'object', 'title': 'cognitive_safe_self_improveDictOutput', 'additionalProperties': True}
cognitive_safe_self_improve
Safe self-improvement: propose modifications guarded by immutable verification oracles and anchor regressions.
Input schema
{'type': 'object', 'title': 'cognitive_safe_self_improveArguments', 'required': ['target_component', 'patch_name', 'proposed_changes'], 'properties': {'rollback': {'type': 'boolean', 'title': 'Rollback', 'default': False}, 'patch_name': {'type': 'string', 'title': 'Patch Name'}, 'proposed_changes': {'type': 'object', 'title': 'Proposed Changes', 'additionalProperties': True}, 'target_component': {'type': 'string', 'title': 'Target Component'}, 'rollback_snapshot_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Rollback Snapshot Id', 'default': None}, 'simulated_regression_fail': {'type': 'boolean', 'title': 'Simulated Regression Fail', 'default': False}}}
Output schema
{'type': 'object', 'title': 'cognitive_safe_self_improveDictOutput', 'additionalProperties': True}
cognitive.simulate_actions
Simulate and rank candidate actions by predicted feasibility, reward, and constraint safety.
Input schema
{'type': 'object', 'title': 'cognitive_simulate_actionsArguments', 'required': ['state', 'candidate_actions'], 'properties': {'dt': {'type': 'number', 'title': 'Dt', 'default': 1.0}, 'state': {'type': 'object', 'title': 'State', 'additionalProperties': True}, 'candidate_actions': {'type': 'array', 'items': {}, 'title': 'Candidate Actions'}}}
Output schema
{'type': 'object', 'title': 'cognitive_simulate_actionsDictOutput', 'additionalProperties': True}
cognitive_simulate_actions
Simulate and rank candidate actions by predicted feasibility, reward, and constraint safety.
Input schema
{'type': 'object', 'title': 'cognitive_simulate_actionsArguments', 'required': ['state', 'candidate_actions'], 'properties': {'dt': {'type': 'number', 'title': 'Dt', 'default': 1.0}, 'state': {'type': 'object', 'title': 'State', 'additionalProperties': True}, 'candidate_actions': {'type': 'array', 'items': {}, 'title': 'Candidate Actions'}}}
Output schema
{'type': 'object', 'title': 'cognitive_simulate_actionsDictOutput', 'additionalProperties': True}
cognitive.solve_and_compare
End-to-end autonomy: identify â guide â execute â baseline â verify â verdict.
Give raw task data (scheduling: workers/shifts/eligibility/capacity/
exclusivity; graph: nodes/edges; allocation: consumers/resources/...).
Returns the guided solution, the unguided baseline, independent
verification of both (with objective_source + independently_verified),
and whether the engine improved the result.
Input schema
{'type': 'object', 'title': 'cognitive_solve_and_compareArguments', 'properties': {'task': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Task', 'default': None}, 'model_family': {'type': 'string', 'title': 'Model Family', 'default': 'generic'}}}
Output schema
{'type': 'object', 'title': 'cognitive_solve_and_compareDictOutput', 'additionalProperties': True}
cognitive_solve_and_compare
End-to-end autonomy: identify â guide â execute â baseline â verify â verdict.
Give raw task data (scheduling: workers/shifts/eligibility/capacity/
exclusivity; graph: nodes/edges; allocation: consumers/resources/...).
Returns the guided solution, the unguided baseline, independent
verification of both (with objective_source + independently_verified),
and whether the engine improved the result.
Input schema
{'type': 'object', 'title': 'cognitive_solve_and_compareArguments', 'properties': {'task': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Task', 'default': None}, 'model_family': {'type': 'string', 'title': 'Model Family', 'default': 'generic'}}}
Output schema
{'type': 'object', 'title': 'cognitive_solve_and_compareDictOutput', 'additionalProperties': True}
cognitive.solve_arithmetic
Evaluate or simplify mathematical expressions (PEMDAS with power, functions like sqrt, exp, log, sin, cos).
Input schema
{'type': 'object', 'title': 'cognitive_solve_arithmeticArguments', 'required': ['expression'], 'properties': {'simplify': {'type': 'boolean', 'title': 'Simplify', 'default': False}, 'variables': {'anyOf': [{'type': 'object', 'additionalProperties': {'type': 'number'}}, {'type': 'null'}], 'title': 'Variables', 'default': None}, 'expression': {'type': 'string', 'title': 'Expression'}}}
Output schema
{'type': 'object', 'title': 'cognitive_solve_arithmeticDictOutput', 'additionalProperties': True}
cognitive_solve_arithmetic
Evaluate or simplify mathematical expressions (PEMDAS with power, functions like sqrt, exp, log, sin, cos).
Input schema
{'type': 'object', 'title': 'cognitive_solve_arithmeticArguments', 'required': ['expression'], 'properties': {'simplify': {'type': 'boolean', 'title': 'Simplify', 'default': False}, 'variables': {'anyOf': [{'type': 'object', 'additionalProperties': {'type': 'number'}}, {'type': 'null'}], 'title': 'Variables', 'default': None}, 'expression': {'type': 'string', 'title': 'Expression'}}}
Output schema
{'type': 'object', 'title': 'cognitive_solve_arithmeticDictOutput', 'additionalProperties': True}
cognitive.solve_equation_system
Solve linear equations (ax + b = c), quadratic equations (ax^2 + bx + c = 0), or linear systems (A x = b).
Input schema
{'type': 'object', 'title': 'cognitive_solve_equation_systemArguments', 'required': ['equation_type'], 'properties': {'quad_a': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Quad A', 'default': None}, 'quad_b': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Quad B', 'default': None}, 'quad_c': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Quad C', 'default': None}, 'linear_a': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Linear A', 'default': None}, 'linear_b': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Linear B', 'default': None}, 'linear_c': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Linear C', 'default': 0.0}, 'matrix_A': {'anyOf': [{'type': 'array', 'items': {'type': 'array', 'items': {'type': 'number'}}}, {'type': 'null'}], 'title': 'Matrix A', 'default': None}, 'vector_b': {'anyOf': [{'type': 'array', 'items': {'type': 'number'}}, {'type': 'null'}], 'title': 'Vector B', 'default': None}, 'equation_type': {'type': 'string', 'title': 'Equation Type'}}}
Output schema
{'type': 'object', 'title': 'cognitive_solve_equation_systemDictOutput', 'additionalProperties': True}
cognitive_solve_equation_system
Solve linear equations (ax + b = c), quadratic equations (ax^2 + bx + c = 0), or linear systems (A x = b).
Input schema
{'type': 'object', 'title': 'cognitive_solve_equation_systemArguments', 'required': ['equation_type'], 'properties': {'quad_a': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Quad A', 'default': None}, 'quad_b': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Quad B', 'default': None}, 'quad_c': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Quad C', 'default': None}, 'linear_a': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Linear A', 'default': None}, 'linear_b': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Linear B', 'default': None}, 'linear_c': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Linear C', 'default': 0.0}, 'matrix_A': {'anyOf': [{'type': 'array', 'items': {'type': 'array', 'items': {'type': 'number'}}}, {'type': 'null'}], 'title': 'Matrix A', 'default': None}, 'vector_b': {'anyOf': [{'type': 'array', 'items': {'type': 'number'}}, {'type': 'null'}], 'title': 'Vector B', 'default': None}, 'equation_type': {'type': 'string', 'title': 'Equation Type'}}}
Output schema
{'type': 'object', 'title': 'cognitive_solve_equation_systemDictOutput', 'additionalProperties': True}
cognitive.solve_word_problem
Solve math word problems (GSM8K/SVAMP/MATH) via topological constraint propagation.
Input schema
{'type': 'object', 'title': 'cognitive_solve_word_problemArguments', 'properties': {'question': {'type': 'string', 'title': 'Question', 'default': ''}, 'equations': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': {'type': 'string'}}}, {'type': 'null'}], 'title': 'Equations', 'default': None}, 'quantities': {'anyOf': [{'type': 'object', 'additionalProperties': {'anyOf': [{'type': 'number'}, {'type': 'integer'}]}}, {'type': 'null'}], 'title': 'Quantities', 'default': None}, 'target_variable': {'type': 'string', 'title': 'Target Variable', 'default': 'target'}}}
Output schema
{'type': 'object', 'title': 'cognitive_solve_word_problemDictOutput', 'additionalProperties': True}
cognitive_solve_word_problem
Solve math word problems (GSM8K/SVAMP/MATH) via topological constraint propagation.
Input schema
{'type': 'object', 'title': 'cognitive_solve_word_problemArguments', 'properties': {'question': {'type': 'string', 'title': 'Question', 'default': ''}, 'equations': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': {'type': 'string'}}}, {'type': 'null'}], 'title': 'Equations', 'default': None}, 'quantities': {'anyOf': [{'type': 'object', 'additionalProperties': {'anyOf': [{'type': 'number'}, {'type': 'integer'}]}}, {'type': 'null'}], 'title': 'Quantities', 'default': None}, 'target_variable': {'type': 'string', 'title': 'Target Variable', 'default': 'target'}}}
Output schema
{'type': 'object', 'title': 'cognitive_solve_word_problemDictOutput', 'additionalProperties': True}
cognitive.start_experience
Start an experience episode (§24, §10).
Does not store raw prompts or full conversations. For long-horizon work,
pass parent_experience_id (+ subgoal) to chain episodes with an inherited
goal stack; unknown parents are rejected, never silently adopted.
Input schema
{'type': 'object', 'title': 'cognitive_start_experienceArguments', 'required': ['task_structure_id', 'environment_id', 'agent_id'], 'properties': {'subgoal': {'type': 'string', 'title': 'Subgoal', 'default': ''}, 'agent_id': {'type': 'string', 'title': 'Agent Id'}, 'model_family': {'type': 'string', 'title': 'Model Family', 'default': 'generic'}, 'model_version': {'type': 'string', 'title': 'Model Version', 'default': '1.0'}, 'environment_id': {'type': 'string', 'title': 'Environment Id'}, 'task_structure_id': {'type': 'string', 'title': 'Task Structure Id'}, 'task_instance_hash': {'type': 'string', 'title': 'Task Instance Hash', 'default': ''}, 'parent_experience_id': {'type': 'string', 'title': 'Parent Experience Id', 'default': ''}}}
Output schema
{'type': 'object', 'title': 'cognitive_start_experienceDictOutput', 'additionalProperties': True}
cognitive_start_experience
Start an experience episode (§24, §10).
Does not store raw prompts or full conversations. For long-horizon work,
pass parent_experience_id (+ subgoal) to chain episodes with an inherited
goal stack; unknown parents are rejected, never silently adopted.
Input schema
{'type': 'object', 'title': 'cognitive_start_experienceArguments', 'required': ['task_structure_id', 'environment_id', 'agent_id'], 'properties': {'subgoal': {'type': 'string', 'title': 'Subgoal', 'default': ''}, 'agent_id': {'type': 'string', 'title': 'Agent Id'}, 'model_family': {'type': 'string', 'title': 'Model Family', 'default': 'generic'}, 'model_version': {'type': 'string', 'title': 'Model Version', 'default': '1.0'}, 'environment_id': {'type': 'string', 'title': 'Environment Id'}, 'task_structure_id': {'type': 'string', 'title': 'Task Structure Id'}, 'task_instance_hash': {'type': 'string', 'title': 'Task Instance Hash', 'default': ''}, 'parent_experience_id': {'type': 'string', 'title': 'Parent Experience Id', 'default': ''}}}
Output schema
{'type': 'object', 'title': 'cognitive_start_experienceDictOutput', 'additionalProperties': True}
cognitive.step_simulated_environment
Step an active simulated environment with an agent action.
Input schema
{'type': 'object', 'title': 'cognitive_step_simulated_environmentArguments', 'required': ['env_id', 'action'], 'properties': {'action': {'type': 'object', 'title': 'Action', 'additionalProperties': True}, 'env_id': {'type': 'string', 'title': 'Env Id'}}}
Output schema
{'type': 'object', 'title': 'cognitive_step_simulated_environmentDictOutput', 'additionalProperties': True}
cognitive_step_simulated_environment
Step an active simulated environment with an agent action.
Input schema
{'type': 'object', 'title': 'cognitive_step_simulated_environmentArguments', 'required': ['env_id', 'action'], 'properties': {'action': {'type': 'object', 'title': 'Action', 'additionalProperties': True}, 'env_id': {'type': 'string', 'title': 'Env Id'}}}
Output schema
{'type': 'object', 'title': 'cognitive_step_simulated_environmentDictOutput', 'additionalProperties': True}
cognitive.submit_outcome
Submit the structured outcome of an experience episode (§24, §10).
Triggering this may induce candidate strategies in the engine.
Input schema
{'type': 'object', 'title': 'cognitive_submit_outcomeArguments', 'required': ['experience_id', 'result', 'verifier_result'], 'properties': {'result': {'type': 'object', 'title': 'Result', 'additionalProperties': True}, 'metrics': {'anyOf': [{'type': 'object', 'additionalProperties': {'type': 'number'}}, {'type': 'null'}], 'title': 'Metrics', 'default': None}, 'success': {'anyOf': [{'type': 'boolean'}, {'type': 'null'}], 'title': 'Success', 'default': None}, 'experience_id': {'type': 'string', 'title': 'Experience Id'}, 'failure_modes': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'Failure Modes', 'default': None}, 'verifier_result': {'type': 'string', 'title': 'Verifier Result'}}}
Output schema
{'type': 'object', 'title': 'cognitive_submit_outcomeDictOutput', 'additionalProperties': True}
cognitive_submit_outcome
Submit the structured outcome of an experience episode (§24, §10).
Triggering this may induce candidate strategies in the engine.
Input schema
{'type': 'object', 'title': 'cognitive_submit_outcomeArguments', 'required': ['experience_id', 'result', 'verifier_result'], 'properties': {'result': {'type': 'object', 'title': 'Result', 'additionalProperties': True}, 'metrics': {'anyOf': [{'type': 'object', 'additionalProperties': {'type': 'number'}}, {'type': 'null'}], 'title': 'Metrics', 'default': None}, 'success': {'anyOf': [{'type': 'boolean'}, {'type': 'null'}], 'title': 'Success', 'default': None}, 'experience_id': {'type': 'string', 'title': 'Experience Id'}, 'failure_modes': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'Failure Modes', 'default': None}, 'verifier_result': {'type': 'string', 'title': 'Verifier Result'}}}
Output schema
{'type': 'object', 'title': 'cognitive_submit_outcomeDictOutput', 'additionalProperties': True}
cognitive.synthesize_program
Synthesize new algorithmic Python procedures on the fly with AST sandboxing and verification.
Input schema
{'type': 'object', 'title': 'cognitive_synthesize_programArguments', 'required': ['problem_type'], 'properties': {'parameters': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Parameters', 'default': None}, 'problem_type': {'type': 'string', 'title': 'Problem Type'}, 'test_examples': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Test Examples', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_synthesize_programDictOutput', 'additionalProperties': True}
cognitive_synthesize_program
Synthesize new algorithmic Python procedures on the fly with AST sandboxing and verification.
Input schema
{'type': 'object', 'title': 'cognitive_synthesize_programArguments', 'required': ['problem_type'], 'properties': {'parameters': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Parameters', 'default': None}, 'problem_type': {'type': 'string', 'title': 'Problem Type'}, 'test_examples': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Test Examples', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_synthesize_programDictOutput', 'additionalProperties': True}
cognitive.synthesize_singular_path
Synthesize an optimal, invariant-verified trajectory from initial state to goal through the singular bottleneck.
Eliminates dead-end branching and hallucinated unfeasible solutions.
Input schema
{'type': 'object', 'title': 'cognitive_synthesize_singular_pathArguments', 'properties': {'task_data': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Task Data', 'default': None}, 'lattice_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Lattice Id', 'default': None}, 'step_budget': {'type': 'integer', 'title': 'Step Budget', 'default': 5}, 'initial_state': {'anyOf': [{'type': 'object', 'additionalProperties': {'type': 'number'}}, {'type': 'null'}], 'title': 'Initial State', 'default': None}, 'goal_conditions': {'anyOf': [{'type': 'object', 'additionalProperties': {'type': 'array', 'items': {'anyOf': [{'type': 'number'}, {'type': 'null'}]}}}, {'type': 'null'}], 'title': 'Goal Conditions', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_synthesize_singular_pathDictOutput', 'additionalProperties': True}
cognitive_synthesize_singular_path
Synthesize an optimal, invariant-verified trajectory from initial state to goal through the singular bottleneck.
Eliminates dead-end branching and hallucinated unfeasible solutions.
Input schema
{'type': 'object', 'title': 'cognitive_synthesize_singular_pathArguments', 'properties': {'task_data': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Task Data', 'default': None}, 'lattice_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Lattice Id', 'default': None}, 'step_budget': {'type': 'integer', 'title': 'Step Budget', 'default': 5}, 'initial_state': {'anyOf': [{'type': 'object', 'additionalProperties': {'type': 'number'}}, {'type': 'null'}], 'title': 'Initial State', 'default': None}, 'goal_conditions': {'anyOf': [{'type': 'object', 'additionalProperties': {'type': 'array', 'items': {'anyOf': [{'type': 'number'}, {'type': 'null'}]}}}, {'type': 'null'}], 'title': 'Goal Conditions', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_synthesize_singular_pathDictOutput', 'additionalProperties': True}
cognitive.theory_of_mind
Theory of Mind: model agents' BDI mental states, test false beliefs (Sally-Anne), and infer goals via inverse planning.
Input schema
{'type': 'object', 'title': 'cognitive_theory_of_mindArguments', 'required': ['agent_id'], 'properties': {'beliefs': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Beliefs', 'default': None}, 'desires': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Desires', 'default': None}, 'agent_id': {'type': 'string', 'title': 'Agent Id'}, 'intentions': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'Intentions', 'default': None}, 'action_trace': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'Action Trace', 'default': None}, 'ground_truth': {'anyOf': [{}, {'type': 'null'}], 'title': 'Ground Truth', 'default': None}, 'witness_event': {'type': 'boolean', 'title': 'Witness Event', 'default': False}, 'candidate_goals': {'anyOf': [{'type': 'object', 'additionalProperties': {'type': 'array', 'items': {'type': 'string'}}}, {'type': 'null'}], 'title': 'Candidate Goals', 'default': None}, 'evaluate_false_belief_fact': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Evaluate False Belief Fact', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_theory_of_mindDictOutput', 'additionalProperties': True}
cognitive_theory_of_mind
Theory of Mind: model agents' BDI mental states, test false beliefs (Sally-Anne), and infer goals via inverse planning.
Input schema
{'type': 'object', 'title': 'cognitive_theory_of_mindArguments', 'required': ['agent_id'], 'properties': {'beliefs': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Beliefs', 'default': None}, 'desires': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Desires', 'default': None}, 'agent_id': {'type': 'string', 'title': 'Agent Id'}, 'intentions': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'Intentions', 'default': None}, 'action_trace': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'Action Trace', 'default': None}, 'ground_truth': {'anyOf': [{}, {'type': 'null'}], 'title': 'Ground Truth', 'default': None}, 'witness_event': {'type': 'boolean', 'title': 'Witness Event', 'default': False}, 'candidate_goals': {'anyOf': [{'type': 'object', 'additionalProperties': {'type': 'array', 'items': {'type': 'string'}}}, {'type': 'null'}], 'title': 'Candidate Goals', 'default': None}, 'evaluate_false_belief_fact': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Evaluate False Belief Fact', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_theory_of_mindDictOutput', 'additionalProperties': True}
cognitive.tree_search
Perform Monte Carlo Tree Search (UCT) over action sequences to find optimal trajectory.
Input schema
{'type': 'object', 'title': 'cognitive_tree_searchArguments', 'properties': {'initial_state': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Initial State', 'default': None}, 'valid_actions': {'anyOf': [{'type': 'array', 'items': {}}, {'type': 'null'}], 'title': 'Valid Actions', 'default': None}, 'max_iterations': {'type': 'integer', 'title': 'Max Iterations', 'default': 100}}}
Output schema
{'type': 'object', 'title': 'cognitive_tree_searchDictOutput', 'additionalProperties': True}
cognitive_tree_search
Perform Monte Carlo Tree Search (UCT) over action sequences to find optimal trajectory.
Input schema
{'type': 'object', 'title': 'cognitive_tree_searchArguments', 'properties': {'initial_state': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Initial State', 'default': None}, 'valid_actions': {'anyOf': [{'type': 'array', 'items': {}}, {'type': 'null'}], 'title': 'Valid Actions', 'default': None}, 'max_iterations': {'type': 'integer', 'title': 'Max Iterations', 'default': 100}}}
Output schema
{'type': 'object', 'title': 'cognitive_tree_searchDictOutput', 'additionalProperties': True}
cognitive.verify_arithmetic_claim
Formally verify an arithmetic equality claim, audit numerical stability, condition number, and cancellation risks.
Input schema
{'type': 'object', 'title': 'cognitive_verify_arithmetic_claimArguments', 'required': ['claim_lhs', 'claim_rhs'], 'properties': {'matrix': {'anyOf': [{'type': 'array', 'items': {'type': 'array', 'items': {'type': 'number'}}}, {'type': 'null'}], 'title': 'Matrix', 'default': None}, 'claim_lhs': {'anyOf': [{'type': 'string'}, {'type': 'number'}], 'title': 'Claim Lhs'}, 'claim_rhs': {'anyOf': [{'type': 'string'}, {'type': 'number'}], 'title': 'Claim Rhs'}, 'tolerance': {'type': 'number', 'title': 'Tolerance', 'default': 1e-06}, 'audit_stability': {'type': 'boolean', 'title': 'Audit Stability', 'default': False}}}
Output schema
{'type': 'object', 'title': 'cognitive_verify_arithmetic_claimDictOutput', 'additionalProperties': True}
cognitive_verify_arithmetic_claim
Formally verify an arithmetic equality claim, audit numerical stability, condition number, and cancellation risks.
Input schema
{'type': 'object', 'title': 'cognitive_verify_arithmetic_claimArguments', 'required': ['claim_lhs', 'claim_rhs'], 'properties': {'matrix': {'anyOf': [{'type': 'array', 'items': {'type': 'array', 'items': {'type': 'number'}}}, {'type': 'null'}], 'title': 'Matrix', 'default': None}, 'claim_lhs': {'anyOf': [{'type': 'string'}, {'type': 'number'}], 'title': 'Claim Lhs'}, 'claim_rhs': {'anyOf': [{'type': 'string'}, {'type': 'number'}], 'title': 'Claim Rhs'}, 'tolerance': {'type': 'number', 'title': 'Tolerance', 'default': 1e-06}, 'audit_stability': {'type': 'boolean', 'title': 'Audit Stability', 'default': False}}}
Output schema
{'type': 'object', 'title': 'cognitive_verify_arithmetic_claimDictOutput', 'additionalProperties': True}
cognitive.verify_ethics_and_norms
Normative ethics & fairness: enforce deontological vetos, evaluate Rawlsian vs Utilitarian welfare, and arbitrate moral dilemmas.
Input schema
{'type': 'object', 'title': 'cognitive_verify_ethics_and_normsArguments', 'properties': {'options': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Options', 'default': None}, 'stakeholder_payoffs': {'anyOf': [{'type': 'object', 'additionalProperties': {'type': 'number'}}, {'type': 'null'}], 'title': 'Stakeholder Payoffs', 'default': None}, 'proposed_action_or_plan': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Proposed Action Or Plan', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_verify_ethics_and_normsDictOutput', 'additionalProperties': True}
cognitive_verify_ethics_and_norms
Normative ethics & fairness: enforce deontological vetos, evaluate Rawlsian vs Utilitarian welfare, and arbitrate moral dilemmas.
Input schema
{'type': 'object', 'title': 'cognitive_verify_ethics_and_normsArguments', 'properties': {'options': {'anyOf': [{'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Options', 'default': None}, 'stakeholder_payoffs': {'anyOf': [{'type': 'object', 'additionalProperties': {'type': 'number'}}, {'type': 'null'}], 'title': 'Stakeholder Payoffs', 'default': None}, 'proposed_action_or_plan': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}, {'type': 'null'}], 'title': 'Proposed Action Or Plan', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_verify_ethics_and_normsDictOutput', 'additionalProperties': True}
cognitive.verify_lattice_transition
Verify a candidate state or transition S_t -> S_{t+1} against invariant boundary manifolds.
Input schema
{'type': 'object', 'title': 'cognitive_verify_lattice_transitionArguments', 'properties': {'action': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Action', 'default': None}, 'lattice_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Lattice Id', 'default': None}, 'next_state': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Next State', 'default': None}, 'prev_state': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Prev State', 'default': None}, 'lattice_data': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Lattice Data', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_verify_lattice_transitionDictOutput', 'additionalProperties': True}
cognitive_verify_lattice_transition
Verify a candidate state or transition S_t -> S_{t+1} against invariant boundary manifolds.
Input schema
{'type': 'object', 'title': 'cognitive_verify_lattice_transitionArguments', 'properties': {'action': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Action', 'default': None}, 'lattice_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Lattice Id', 'default': None}, 'next_state': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Next State', 'default': None}, 'prev_state': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Prev State', 'default': None}, 'lattice_data': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Lattice Data', 'default': None}}}
Output schema
{'type': 'object', 'title': 'cognitive_verify_lattice_transitionDictOutput', 'additionalProperties': True}
cognitive.verify_strategy
Run objective deterministic verification on a strategy (§24, §16).
Clients cannot self-promote. Verification is evaluated server-side.
Pass task_structure_id (from cognitive.identify_task) so constraints are
independently recomputed from registered descriptors instead of trusting
trace flags. Objective precedence: explicit caller value â recomputed from
raw data â registered spec (labeled unknown) â nested trace claims ONLY
when trust_trace_objective=true â otherwise unknown, never silent 0.0.
Returns passed/score plus details.objective_source and
details.independently_verified so callers know what was recomputed
versus taken on trace claims. Failures are structured, never bare.
Input schema
{'type': 'object', 'title': 'cognitive_verify_strategyArguments', 'required': ['strategy_id'], 'properties': {'strategy_id': {'type': 'string', 'title': 'Strategy Id'}, 'task_instance': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Task Instance', 'default': None}, 'execution_trace': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Execution Trace', 'default': None}, 'task_structure_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Task Structure Id', 'default': None}, 'trust_trace_objective': {'type': 'boolean', 'title': 'Trust Trace Objective', 'default': False}}}
Output schema
{'type': 'object', 'title': 'cognitive_verify_strategyDictOutput', 'additionalProperties': True}
cognitive_verify_strategy
Run objective deterministic verification on a strategy (§24, §16).
Clients cannot self-promote. Verification is evaluated server-side.
Pass task_structure_id (from cognitive.identify_task) so constraints are
independently recomputed from registered descriptors instead of trusting
trace flags. Objective precedence: explicit caller value â recomputed from
raw data â registered spec (labeled unknown) â nested trace claims ONLY
when trust_trace_objective=true â otherwise unknown, never silent 0.0.
Returns passed/score plus details.objective_source and
details.independently_verified so callers know what was recomputed
versus taken on trace claims. Failures are structured, never bare.
Input schema
{'type': 'object', 'title': 'cognitive_verify_strategyArguments', 'required': ['strategy_id'], 'properties': {'strategy_id': {'type': 'string', 'title': 'Strategy Id'}, 'task_instance': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Task Instance', 'default': None}, 'execution_trace': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Execution Trace', 'default': None}, 'task_structure_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Task Structure Id', 'default': None}, 'trust_trace_objective': {'type': 'boolean', 'title': 'Trust Trace Objective', 'default': False}}}
Output schema
{'type': 'object', 'title': 'cognitive_verify_strategyDictOutput', 'additionalProperties': True}
History
Recent tool changes
Added
cognitive_crucible_stress_test
Sept. 25, 2026, 2:46 a.m.
Added
cognitive_refine_lattice_from_feedback
Sept. 25, 2026, 2:46 a.m.
Added
cognitive_induce_morphic_transfer
Sept. 25, 2026, 2:46 a.m.
Added
cognitive_compute_lattice_signature
Sept. 25, 2026, 2:46 a.m.
Added
cognitive_synthesize_singular_path
Sept. 25, 2026, 2:46 a.m.
Added
cognitive_project_to_manifold
Sept. 25, 2026, 2:46 a.m.
Added
cognitive_verify_lattice_transition
Sept. 25, 2026, 2:46 a.m.
Added
cognitive_compile_invariant_lattice
Sept. 25, 2026, 2:46 a.m.
Added
cognitive_evaluate_counterfactual_query
Sept. 25, 2026, 2:46 a.m.
Added
cognitive_plan_with_counterfactuals
Sept. 25, 2026, 2:46 a.m.
Added
cognitive_evaluate_claim_evidence
Sept. 25, 2026, 2:46 a.m.
Added
cognitive_get_final_evidence_result
Sept. 25, 2026, 2:46 a.m.
Added
cognitive_audit_evidence_graph
Sept. 25, 2026, 2:46 a.m.
Added
cognitive_build_evidence_graph
Sept. 25, 2026, 2:46 a.m.
Added
cognitive_evaluate_generalization_benchmarks
Sept. 25, 2026, 2:46 a.m.
Added
cognitive_run_multi_agent_simulation
Sept. 25, 2026, 2:46 a.m.
Added
cognitive_run_closed_loop_agent
Sept. 25, 2026, 2:46 a.m.
Added
cognitive_step_simulated_environment
Sept. 25, 2026, 2:46 a.m.
Added
cognitive_create_simulated_environment
Sept. 25, 2026, 2:46 a.m.
Added
cognitive_verify_arithmetic_claim
Sept. 25, 2026, 2:46 a.m.
Added
cognitive_matrix_algebra
Sept. 25, 2026, 2:46 a.m.
Added
cognitive_compute_number_theory
Sept. 25, 2026, 2:46 a.m.
Added
cognitive_solve_word_problem
Sept. 25, 2026, 2:46 a.m.
Added
cognitive_solve_equation_system
Sept. 25, 2026, 2:46 a.m.
Added
cognitive_solve_arithmetic
Sept. 25, 2026, 2:46 a.m.
Added
cognitive_arbitrate_temporal_objectives
Sept. 25, 2026, 2:46 a.m.
Added
cognitive_infer_human_values
Sept. 25, 2026, 2:46 a.m.
Added
cognitive_generate_and_prioritize_goals
Sept. 25, 2026, 2:46 a.m.
Added
cognitive_compute_intrinsic_rewards
Sept. 25, 2026, 2:46 a.m.
Added
cognitive_inspect_lexicon
Sept. 25, 2026, 2:46 a.m.
Discover
Similar MCP servers
AdButler
com.adbutler/mcp-server
Manages AdButler advertisers, campaigns, ad items, audiences, zones, creatives, catalogs, and advertising reports.
Meta Council
com.meta-council/decision-intelligence
Provides governed workflows for consulting, outreach, sales deals, invoices, marketing campaigns, content approvals, accounting a…
synter-ads
io.github.Synter-Media-AI/synter-ads
Manages cross-platform advertising campaigns, audiences, tracking, creative, landing pages, campaign audits, technology intellige…
synter-ads
io.github.jshorwitz/synter-ads
Manages cross-platform advertising campaigns, audiences, tracking, creative, landing pages, campaign audits, technology intellige…
Stratalize Intelligence
com.stratalize/intelligence
Provides vendor, market, SaaS, workforce, government contract, public company, regulatory, competitive, and AI-brand intelligence…
CorpusIQ
io.corpusiq/multi-source-mcp
Connects authenticated users to business systems such as CRM, ecommerce, advertising, analytics, email marketing, SEO, calendars,…
Riley Craig x402 Agent Store
io.github.rccola990-cloud/x402-agent-store
Provides paid blockchain data and transaction reads, AI brand-visibility research, and company or lead enrichment services.
GrowthKit Revenue Intelligence
tools.growthkit/revenue-intelligence
Supports B2B revenue operations with lead campaigns, ICP and company enrichment, CRM records and activities, outreach drafting, t…