What this MCP does
Provides agent infrastructure for deduplication, cost prediction, validation, governance, and failure analysis.
Capabilities
Tools
15
tools
burnrate_budget
Get today's tracked LLM spend, per-model breakdown, projection, and budget alerts. Free — no credits charged.
Read only
Idempotent
Input schema
{'type': 'object', 'properties': {'daily_limit': {'type': 'number', 'description': 'Optional. Daily budget in USD (e.g., 10.0 for a $10/day cap). Enables budget alerts and remaining-balance calculation.'}}, 'additionalProperties': False}
Output schema
{'type': 'object', 'properties': {'date': {'type': 'string'}, 'spend': {'type': 'object', 'properties': {'by_model': {'type': 'array', 'items': {'type': 'object'}}, 'total_usd': {'type': 'number'}, 'total_calls': {'type': 'number'}, 'total_usd_formatted': {'type': 'string'}}}, 'alerts': {'type': 'array', 'items': {'type': 'string'}}, 'projection': {'type': 'object', 'properties': {'hourly_rate_usd': {'type': 'number'}, 'hours_remaining': {'type': 'number'}, 'projected_day_total_usd': {'type': 'number'}}}, 'request_id': {'type': 'string'}, 'credits_used': {'type': 'number'}, 'credits_remaining': {'type': 'number'}, 'fallback_behavior': {'type': 'string'}}}
burnrate_estimate
Before executing a multi-step agent plan, estimate the total LLM cost. Returns per-step breakdown and optimization suggestions. If the estimate exceeds your budget, pipe the same plan into burnrate_optimize. Costs 1 credit.
Idempotent
Input schema
{'type': 'object', 'required': ['plan'], 'properties': {'plan': {'type': 'array', 'items': {'type': 'object', 'required': ['step', 'provider', 'model', 'estimated_input_tokens', 'estimated_output_tokens'], 'properties': {'step': {'type': 'string', 'description': 'Step identifier — string label or number.'}, 'model': {'type': 'string', 'description': 'Model name: claude-sonnet-4-6, gpt-4o, etc.'}, 'provider': {'type': 'string', 'description': 'LLM provider: anthropic, openai, google, etc.'}, 'search_calls': {'type': 'number', 'description': 'Optional. Number of grounding/search calls (e.g. Gemini Search).'}, 'cache_read_tokens': {'type': 'number', 'description': 'Optional. Cached prompt tokens for providers with prompt caching.'}, 'estimated_input_tokens': {'type': 'number', 'description': 'Estimated prompt token count.'}, 'estimated_output_tokens': {'type': 'number', 'description': 'Estimated completion token count.'}}}, 'description': 'Array of plan steps with provider, model, and token estimates.'}}, 'additionalProperties': False}
Output schema
{'type': 'object', 'properties': {'estimate': {'type': 'object', 'properties': {'steps': {'type': 'array', 'items': {'type': 'object'}}, 'step_count': {'type': 'number'}, 'pricing_as_of': {'type': 'string'}, 'pricing_source': {'type': 'string'}, 'total_cost_usd': {'type': 'number'}, 'estimate_complete': {'type': 'boolean'}, 'unrecognized_models': {'type': 'array', 'items': {'type': 'string'}}, 'recognized_step_count': {'type': 'number'}, 'total_cost_usd_formatted': {'type': 'string'}}}, 'request_id': {'type': 'string'}, 'credits_used': {'type': 'number'}, 'credits_remaining': {'type': 'number'}, 'fallback_behavior': {'type': 'string'}, 'optimization_suggestions': {'type': 'array', 'items': {'type': 'string'}}}}
burnrate_optimize
Get a cheaper equivalent plan by substituting models with lower-cost alternatives. Call after burnrate_estimate if the estimated cost exceeds your budget. Returns the optimized plan with substituted models, new per-step costs, total savings, and whether the target_budget is met. Optionally set target_budget to constrain the optimization. Costs 1 credit.
Idempotent
Input schema
{'type': 'object', 'required': ['plan'], 'properties': {'plan': {'type': 'array', 'items': {'type': 'object', 'required': ['step', 'provider', 'model', 'estimated_input_tokens', 'estimated_output_tokens'], 'properties': {'step': {'type': 'string', 'description': 'Step identifier — string label or number.'}, 'model': {'type': 'string', 'description': 'Model name.'}, 'provider': {'type': 'string', 'description': 'LLM provider.'}, 'estimated_input_tokens': {'type': 'number', 'description': 'Estimated input tokens.'}, 'estimated_output_tokens': {'type': 'number', 'description': 'Estimated output tokens.'}}}, 'description': 'Array of plan steps. Same schema as burnrate_estimate: each step needs step, provider, model, estimated_input_tokens, estimated_output_tokens.'}, 'target_budget': {'type': 'number', 'description': 'Optional. Target total cost in USD.'}}, 'additionalProperties': False}
Output schema
{'type': 'object', 'properties': {'steps': {'type': 'array', 'items': {'type': 'object'}}, 'original': {'type': 'object', 'properties': {'total_cost_usd': {'type': 'number'}, 'total_cost_usd_formatted': {'type': 'string'}}}, 'optimized': {'type': 'object', 'properties': {'total_cost_usd': {'type': 'number'}, 'total_savings_pct': {'type': 'number'}, 'total_savings_usd': {'type': 'number'}, 'total_cost_usd_formatted': {'type': 'string'}}}, 'request_id': {'type': 'string'}, 'suggestions': {'type': 'array', 'items': {'type': 'string'}}, 'credits_used': {'type': 'number'}, 'credits_remaining': {'type': 'number'}, 'fallback_behavior': {'type': 'string'}}}
burnrate_track
Log the actual cost of an LLM call after execution. Call this after every LLM request to build calibration data that improves burnrate_estimate accuracy over time. Free — no credits charged. Returns the recorded cost entry with computed margin versus the prior estimate when one exists for this model and token range.
Idempotent
Input schema
{'type': 'object', 'required': ['provider', 'model', 'input_tokens', 'output_tokens'], 'properties': {'model': {'type': 'string', 'description': 'Model identifier as returned by the provider. Examples: claude-sonnet-4-6, gpt-4o, gemini-2.0-flash, mistral-large-latest. Unknown models are accepted but cost may show as $0.'}, 'task_id': {'type': 'string', 'description': 'Optional task ID for cross-referencing spend with DedupQ deduplication results. Use the same task_id passed to dedupq_check to link cost tracking with deduplication.'}, 'provider': {'type': 'string', 'description': 'LLM provider identifier. Supported: anthropic, openai, google, mistral, cohere, deepseek, together, fireworks, groq. Must match the provider of the model used.'}, 'input_tokens': {'type': 'number', 'description': 'Actual prompt tokens used. Must be >= 0.'}, 'output_tokens': {'type': 'number', 'description': 'Actual completion tokens used. Must be >= 0.'}, 'cache_read_tokens': {'type': 'number', 'description': 'Optional. Cache-read tokens.'}}, 'additionalProperties': False}
Output schema
{'type': 'object', 'properties': {'model': {'type': 'string'}, 'tracked': {'type': 'boolean'}, 'provider': {'type': 'string'}, 'record_id': {'type': 'string'}, 'request_id': {'type': 'string'}, 'credits_used': {'type': 'number'}, 'input_tokens': {'type': 'number'}, 'output_tokens': {'type': 'number'}, 'pricing_found': {'type': 'boolean'}, 'actual_cost_usd': {'type': 'number'}, 'credits_remaining': {'type': 'number'}, 'fallback_behavior': {'type': 'string'}, 'actual_cost_usd_formatted': {'type': 'string'}}}
dedupq_check
Before executing any LLM task, check if an identical or semantically similar task has already been completed. Returns cached result on hit, saving one LLM call. On a miss, execute your task and call dedupq_complete to cache the result for future hits. Costs 1 credit.
Open world
Idempotent
Input schema
{'type': 'object', 'required': ['content'], 'properties': {'content': {'type': 'string', 'description': 'The task content to check for duplicates. This is hashed and embedded for matching.'}, 'task_id': {'type': 'string', 'description': 'Optional caller task ID for tracing and cross-referencing with BurnRate.'}, 'hash_only': {'type': 'boolean', 'description': 'If true, skip vector similarity search and use exact hash matching only. Default: false.'}, 'similarity_threshold': {'type': 'number', 'description': 'Cosine similarity threshold for semantic matching, 0.0 to 1.0. Default: 0.80.'}}, 'additionalProperties': False}
Output schema
{'type': 'object', 'properties': {'match': {'type': 'object', 'properties': {'task_id': {'type': 'string'}, 'cached_at': {'type': 'string'}, 'match_type': {'type': 'string'}, 'similarity': {'type': 'number'}}}, 'status': {'type': 'string', 'description': 'hit | miss | in_progress'}, 'cache_hit': {'type': 'string'}, 'request_id': {'type': 'string'}, 'content_hash': {'type': 'string'}, 'credits_used': {'type': 'number'}, 'cache_age_seconds': {'type': 'number'}, 'credits_remaining': {'type': 'number'}, 'fallback_behavior': {'type': 'string'}}}
dedupq_complete
After executing a task, store the result so future identical or similar tasks return a cache hit via dedupq_check. Costs 2 credits.
Open world
Idempotent
Input schema
{'type': 'object', 'required': ['content', 'result'], 'properties': {'result': {'description': 'The task result to cache. Can be any JSON value.'}, 'content': {'type': 'string', 'description': 'Original task content. Used to compute hash and embedding for future matching.'}, 'task_id': {'type': 'string', 'description': 'Optional task ID. Used as the database row ID if provided.'}, 'hash_only': {'type': 'boolean', 'description': 'If true, skip embedding generation. Default: false.'}}, 'additionalProperties': False}
Output schema
{'type': 'object', 'properties': {'stored': {'type': 'boolean'}, 'task_id': {'type': 'string'}, 'request_id': {'type': 'string'}, 'content_hash': {'type': 'string'}, 'credits_used': {'type': 'number'}, 'has_embedding': {'type': 'boolean'}, 'credits_remaining': {'type': 'number'}, 'fallback_behavior': {'type': 'string'}}}
guardrail_check
Evaluate a proposed agent action against your governance policies. Returns allow or deny with the matched policy reason. Requires at least one active policy created via guardrail_create_policy. Deterministic rule evaluation — no LLM. Costs 1 credit.
Idempotent
Input schema
{'type': 'object', 'required': ['agent_id', 'proposed_action'], 'properties': {'agent_id': {'type': 'string', 'description': 'Agent identifier.'}, 'proposed_action': {'type': 'object', 'required': ['type'], 'properties': {'type': {'type': 'string', 'description': 'Action type: http_request, delete_file, send_email, etc.'}}, 'description': 'Action to evaluate. Must contain a \'type\' field. Example: {"type": "http_request", "url": "https://external.example.com"} or {"type": "file_write", "path": "/etc/config"}.'}}, 'additionalProperties': False}
Output schema
{'type': 'object', 'properties': {'reason': {'type': 'string'}, 'audit_id': {'type': 'string'}, 'decision': {'enum': ['allow', 'deny', 'stub'], 'type': 'string'}, 'policy_id': {'type': 'string'}, 'request_id': {'type': 'string'}, 'credits_used': {'type': 'number'}, 'credits_remaining': {'type': 'number'}, 'fallback_behavior': {'type': 'string'}}}
guardrail_create_policy
Create a persistent governance policy that guardrail_check evaluates on every subsequent call. Define rules using and/or/not operators over action types, resource patterns, and budget thresholds. Call this before using guardrail_check — checks require at least one active policy. Policies persist until explicitly deleted. Duplicate policy names return an error. Returns the created policy with its ID and active status.
Input schema
{'type': 'object', 'required': ['name', 'rules'], 'properties': {'name': {'type': 'string', 'description': "Unique policy name per org. Examples: 'no-delete-in-prod', 'budget-cap-50', 'pii-block'."}, 'rules': {'type': 'array', 'items': {'type': 'object', 'required': ['operator'], 'properties': {'field': {'type': 'string', 'description': "Field path on proposed_action (e.g. 'action_type', 'path', 'amount')."}, 'rules': {'type': 'array', 'description': 'Nested rules for compound operators (and/or/not).'}, 'value': {'type': 'string', 'description': 'Comparison value for leaf operators. String, number, or boolean as string.'}, 'operator': {'type': 'string', 'description': 'Rule operator: eq | starts_with | contains | gt | lt | and | or | not.'}}}, 'description': "Array of rule objects evaluated against the proposed_action in guardrail_check. Leaf operators: eq, starts_with, contains, gt, lt (compare field to value). Compound operators: and, or, not (nest sub-rules in a rules array). Example: [{operator:'eq', field:'type', value:'file_write'}] blocks all file writes. Nested example: [{operator:'and', rules:[{operator:'eq',field:'type',value:'api_call'},{operator:'contains',field:'url',value:'prod'}]}] blocks prod API calls."}, 'priority': {'type': 'number', 'description': 'Optional. Evaluation order. Default: 0.'}, 'description': {'type': 'string', 'description': 'Optional human-readable summary of what this policy enforces. Returned in guardrail_check responses and guardrail_list_policies output for auditability.'}, 'action_types': {'type': 'array', 'items': {'type': 'string'}, 'description': "Optional. Restrict this policy to only evaluate when proposed_action.type matches one of these values. Examples: ['file_write', 'api_call', 'db_delete']. Omit to apply the policy to all action types regardless of type field."}}, 'additionalProperties': False}
Output schema
{'type': 'object', 'properties': {'policy': {'type': 'object', 'properties': {'id': {'type': 'string'}, 'name': {'type': 'string'}, 'rules': {'type': 'array'}, 'enabled': {'type': 'boolean'}, 'priority': {'type': 'number'}, 'created_at': {'type': 'string'}, 'description': {'type': 'string'}, 'action_types': {'type': 'array', 'items': {'type': 'string'}}}}, 'request_id': {'type': 'string'}, 'credits_used': {'type': 'number'}, 'credits_remaining': {'type': 'number'}, 'fallback_behavior': {'type': 'string'}}}
pitfalldb_query
Check for known failure patterns before executing a task type. Returns pitfalls with severity, fix suggestions, and confidence scores. After your agent runs, submit failures via pitfalldb_report so others benefit. Costs 2 credits.
Open world
Idempotent
Input schema
{'type': 'object', 'required': ['task_type'], 'properties': {'filters': {'type': 'object', 'properties': {'language': {'type': 'string', 'description': 'Programming language.'}, 'provider': {'type': 'string', 'description': 'LLM provider.'}, 'framework': {'type': 'string', 'description': 'Framework.'}}, 'description': 'Optional filters.'}, 'task_type': {'type': 'string', 'description': 'Task category: code_generation, web_search, data_analysis, etc.'}, 'task_description': {'type': 'string', 'description': 'Optional. Natural-language task description for semantic search.'}}, 'additionalProperties': False}
Output schema
{'type': 'object', 'properties': {'pitfalls': {'type': 'array', 'items': {'type': 'object', 'properties': {'id': {'type': 'string'}, 'fix': {'type': 'string'}, 'tags': {'type': 'array', 'items': {'type': 'string'}}, 'title': {'type': 'string'}, 'reports': {'type': 'number'}, 'severity': {'type': 'string'}, 'frequency': {'type': 'string'}, 'description': {'type': 'string'}, 'fix_confidence': {'type': 'number'}}}}, 'request_id': {'type': 'string'}, 'credits_used': {'type': 'number'}, 'total_matching': {'type': 'number'}, 'credits_remaining': {'type': 'number'}, 'fallback_behavior': {'type': 'string'}}}
pitfalldb_report
Report an agent failure. PII-scrubbed before storage. Linked to existing pitfalls if similar. Free — no credits charged.
Open world
Idempotent
Input schema
{'type': 'object', 'required': ['task_type', 'task_description', 'failure'], 'properties': {'failure': {'type': 'object', 'properties': {'language': {'type': 'string', 'description': 'Programming language.'}, 'provider': {'type': 'string', 'description': 'LLM provider.'}, 'framework': {'type': 'string', 'description': 'Framework.'}, 'error_type': {'type': 'string', 'description': 'Error category: tool_call_ignored, syntax_error, etc.'}, 'fix_worked': {'type': 'boolean', 'description': 'Whether the fix worked.'}, 'root_cause': {'type': 'string', 'description': 'Root cause analysis (PII-scrubbed).'}, 'fix_applied': {'type': 'string', 'description': 'Fix applied (PII-scrubbed).'}, 'error_message': {'type': 'string', 'description': 'Error message (PII-scrubbed).'}}, 'description': 'Failure details.'}, 'task_type': {'type': 'string', 'description': 'Task category.'}, 'task_description': {'type': 'string', 'description': 'Description of the failed task.'}}, 'additionalProperties': False}
Output schema
{'type': 'object', 'properties': {'message': {'type': 'string'}, 'verified': {'type': 'boolean'}, 'report_id': {'type': 'string'}, 'request_id': {'type': 'string'}, 'credits_used': {'type': 'number'}, 'credits_remaining': {'type': 'number'}, 'fallback_behavior': {'type': 'string'}, 'linked_pitfall_id': {'type': 'string'}}}
qualitygate_validate
After your agent generates output, validate it against your rules before shipping. Runs deterministic checks (regex, JSON schema, syntax) plus optional LLM-powered tone and factual analysis. Returns a structured verdict (pass, warn, or fail) with a 0-100 score and per-check issue details. Use qualitygate_trends to spot recurring failure patterns over time. Variable cost: 1 credit per deterministic check, 8 credits per LLM check.
Open world
Idempotent
Input schema
{'type': 'object', 'required': ['output'], 'properties': {'output': {'type': 'string', 'description': 'The agent output text to validate.'}, 'schema': {'type': 'object', 'description': 'JSON Schema to validate output against.'}, 'language': {'type': 'string', 'description': 'Code language for syntax check: json, python, javascript, typescript.'}, 'override': {'type': 'boolean', 'description': 'Force pass. Requires override_reason.'}, 'directives': {'type': 'array', 'items': {'type': 'object', 'required': ['type', 'value'], 'properties': {'name': {'type': 'string', 'description': 'Optional directive name.'}, 'type': {'type': 'string', 'description': 'Directive type.'}, 'value': {'type': 'string', 'description': 'Directive value — string pattern, regex, keyword array (as comma-separated string), or number (as string). Interpreted based on directive type.'}}}, 'description': 'Directive objects. Types: must_include, must_not_include, must_match, must_not_match, must_contain, must_not_contain, min_length, max_length.'}, 'check_types': {'type': 'array', 'items': {'enum': ['directive_compliance', 'schema_validation', 'code_syntax', 'tone', 'factual_claims'], 'type': 'string'}, 'description': 'Checks to run. Auto-inferred if omitted.'}, 'override_reason': {'type': 'string', 'description': 'Required when override is true.'}}, 'additionalProperties': False}
Output schema
{'type': 'object', 'properties': {'issues': {'type': 'array', 'items': {'type': 'object', 'properties': {'type': {'type': 'string'}, 'message': {'type': 'string'}, 'severity': {'type': 'string'}}}}, 'summary': {'type': 'object', 'properties': {'total': {'type': 'number'}, 'errors': {'type': 'number'}, 'warnings': {'type': 'number'}}}, 'verdict': {'enum': ['pass', 'warn', 'fail'], 'type': 'string'}, 'checks_run': {'type': 'array', 'items': {'type': 'string'}}, 'request_id': {'type': 'string'}, 'credits_used': {'type': 'number'}, 'credits_remaining': {'type': 'number'}, 'fallback_behavior': {'type': 'string'}}}
rigor_execute
Execute a structured workflow end-to-end. Call rigor_plan first (free) to preview the step sequence and cost estimate before committing credits. Classifies the task, selects the optimal tool sequence, and executes each step with the right LLM model. Returns a complete deliverable — solution designs, competitive analyses, governance documents, and more. Supports SSE streaming for real-time progress, webhook callback, or polling. For atomic work — classification, scoring, ranking, entity extraction, query parsing — set preferences.execution to 'direct' and declare preferences.output_contract to get validated JSON records from a single call, routed to the cheapest model that holds the schema.
Open world
Input schema
{'type': 'object', 'required': ['task_description'], 'properties': {'context': {'type': 'object', 'properties': {'additional_context': {'type': 'string', 'description': 'Free-form context the workflow steps can reference.'}}, 'description': 'Additional context for the workflow.'}, 'delivery': {'type': 'object', 'properties': {'method': {'type': 'string', 'description': 'sse | webhook | polling. Default for MCP: polling.'}, 'webhook_url': {'type': 'string', 'description': 'Required if method is webhook. Must be HTTPS.'}}, 'description': "Delivery method. Default: polling (MCP clients typically can't handle SSE)."}, 'task_type': {'type': 'string', 'description': "Optional hint to bypass automatic classification. Passing it also removes the slowest classification tiers from the critical path, so send it whenever you know the shape. Multi-step deliverable types: solution_design, requirements_analysis, code_implementation, code_review, bug_fix, root_cause_analysis, incident_response, deployment_execution, competitive_scan, financial_analysis, research_task, documentation, governance_change, compliance_audit, data_security_assessment, performance_optimization, user_story_definition, implementation_prompt_generation. Atomic single-call types, which auto-select direct execution: tag, score, rerank, compose, extract_entities, parse_query, quick_research, quick_classification, quick_extraction, quick_scoring. Call GET /api/rigor/task-types for the full vocabulary with each type's shape."}, 'preferences': {'type': 'object', 'properties': {'execution': {'enum': ['direct'], 'type': 'string', 'description': "Set to 'direct' to compose the plan's content frameworks into a single LLM call and route cost-first, using a per-task-type model floor that moves when a cheaper model earns the work. Research steps, process steps (classification-verify, review protocol, synthesis) and the quality review each remain separate calls, so this is not a one-call-per-workflow guarantee: for atomic task types, which have a single content framework, the call count matches standard execution and the saving is the model. Supplying output_contract replaces the quality-review call with deterministic validation, which is one fewer call. No intermediate outputs. Available at every tier. Auto-selected for atomic task types when no execution preference is given. Falls back to standard execution when combined with require_approval or interactive mode, or when the plan exceeds the composition size limit. Attachments and prior_workflow_id chaining are NOT applied — use standard execution for those."}, 'rigor_level': {'type': 'string', 'description': 'quick | standard (default) | thorough. Controls analysis depth and cost.'}, 'max_budget_usd': {'type': 'number', 'description': 'Maximum budget in USD.'}, 'output_contract': {'type': 'object', 'required': ['task_type', 'shape', 'fields'], 'properties': {'count': {'type': 'object', 'properties': {'max': {'type': 'number'}, 'min': {'type': 'number'}}, 'description': 'Bounds on the number of entries. Only read when shape is "array".'}, 'shape': {'enum': ['object', 'array'], 'type': 'string', 'description': '"object" for 1 record, "array" for 1 entry per input item.'}, 'fields': {'type': 'array', 'items': {'type': 'object', 'required': ['name', 'type'], 'properties': {'enum': {'type': 'array', 'items': {'type': 'string'}, 'description': 'Restricts a string field to a fixed set of values.'}, 'name': {'type': 'string', 'description': 'The JSON key.'}, 'type': {'enum': ['string', 'number', 'integer', 'boolean', 'array', 'object'], 'type': 'string', 'description': "The value's type."}, 'maximum': {'type': 'number', 'description': 'Upper bound for a numeric field.'}, 'minimum': {'type': 'number', 'description': 'Lower bound for a numeric field.'}, 'required': {'type': 'boolean', 'description': 'Defaults to true. Set false for a field that may be absent.'}, 'description': {'type': 'string', 'description': "Passed to the model as the field's description."}}}, 'description': 'The schema for 1 record.'}, 'selection': {'type': 'object', 'properties': {'maxCount': {'type': 'number'}, 'minCount': {'type': 'number'}, 'minScore': {'type': 'number'}, 'scoreField': {'type': 'string'}}, 'description': 'Use when you want the model to over-generate candidates and Rigor to sort, threshold, and cap them before the count bounds are checked.'}, 'task_type': {'type': 'string', 'description': 'Your own label for the work. Echoed into telemetry. Not read as a framework name and does not change routing.'}}, 'description': 'Only read when execution is "direct". Declares the JSON shape you want back, so the answer is generated against your schema and validated against it before return, instead of returned as prose you have to parse. A conforming run also skips the quality-review call, costing 1 LLM call rather than 2. The schema is closed: a record carrying an undeclared key is rejected exactly like one missing a required key.'}}, 'description': 'Optional workflow preferences.'}, 'task_description': {'type': 'string', 'description': "Natural language description of the task. Be specific — include what you want produced, constraints, and context. Example: 'Design a caching layer for our API gateway with Redis integration.'"}}, 'additionalProperties': False}
Output schema
{'type': 'object', 'properties': {'ok': {'type': 'boolean'}, 'status': {'type': 'string'}, 'poll_url': {'type': 'string'}, 'execution': {'type': 'string', 'description': "Present with value 'direct' when direct execution ran. Absent for standard multi-call execution."}, 'task_type': {'type': 'string'}, 'value_class': {'type': 'string'}, 'workflow_id': {'type': 'string'}, 'delivery_mode': {'type': 'string'}, 'available_modes': {'type': 'array', 'items': {'type': 'string'}}, 'estimated_credits': {'type': 'number'}, 'execution_fallback': {'type': 'boolean', 'description': 'True when you explicitly requested direct execution and it could not be honoured — the workflow ran as standard multi-call instead. Never set for an auto-selected attempt, since you did not ask.'}}}
rigor_plan
Before executing a complex task, get a structured workflow plan with per-step cost estimates. Classifies your task, selects the optimal framework sequence, and returns the full plan without executing anything. The response's allowed_modes tells you whether this plan is eligible for direct execution. Free — no credits charged.
Read only
Open world
Idempotent
Input schema
{'type': 'object', 'required': ['task_description'], 'properties': {'task_type': {'type': 'string', 'description': "Optional hint to bypass automatic classification. Passing it also removes the slowest classification tiers from the critical path, so send it whenever you know the shape. Multi-step deliverable types: solution_design, requirements_analysis, code_implementation, code_review, bug_fix, root_cause_analysis, incident_response, deployment_execution, competitive_scan, financial_analysis, research_task, documentation, governance_change, compliance_audit, data_security_assessment, performance_optimization, user_story_definition, implementation_prompt_generation. Atomic single-call types, which auto-select direct execution: tag, score, rerank, compose, extract_entities, parse_query, quick_research, quick_classification, quick_extraction, quick_scoring. Call GET /api/rigor/task-types for the full vocabulary with each type's shape."}, 'preferences': {'type': 'object', 'properties': {'execution': {'enum': ['direct'], 'type': 'string', 'description': "Set to 'direct' to compose the plan's content frameworks into a single LLM call and route cost-first, using a per-task-type model floor that moves when a cheaper model earns the work. Research steps, process steps (classification-verify, review protocol, synthesis) and the quality review each remain separate calls, so this is not a one-call-per-workflow guarantee: for atomic task types, which have a single content framework, the call count matches standard execution and the saving is the model. Supplying output_contract replaces the quality-review call with deterministic validation, which is one fewer call. No intermediate outputs. Available at every tier. Auto-selected for atomic task types when no execution preference is given. Falls back to standard execution when combined with require_approval or interactive mode, or when the plan exceeds the composition size limit. Attachments and prior_workflow_id chaining are NOT applied — use standard execution for those."}, 'rigor_level': {'enum': ['quick', 'standard', 'thorough'], 'type': 'string', 'description': 'Review depth. Default auto-detected from task complexity.'}, 'add_frameworks': {'type': 'array', 'items': {'type': 'string'}, 'description': 'Inject additional frameworks into the plan.'}, 'max_budget_usd': {'type': 'number', 'description': 'Budget ceiling in USD. Triggers warning if plan exceeds this.'}, 'only_frameworks': {'type': 'array', 'items': {'type': 'string'}, 'description': 'Restrict plan to only these frameworks (mutex with skip_frameworks).'}, 'output_contract': {'type': 'object', 'required': ['task_type', 'shape', 'fields'], 'properties': {'count': {'type': 'object', 'properties': {'max': {'type': 'number'}, 'min': {'type': 'number'}}, 'description': 'Bounds on the number of entries. Only read when shape is "array".'}, 'shape': {'enum': ['object', 'array'], 'type': 'string', 'description': '"object" for 1 record, "array" for 1 entry per input item.'}, 'fields': {'type': 'array', 'items': {'type': 'object', 'required': ['name', 'type'], 'properties': {'enum': {'type': 'array', 'items': {'type': 'string'}, 'description': 'Restricts a string field to a fixed set of values.'}, 'name': {'type': 'string', 'description': 'The JSON key.'}, 'type': {'enum': ['string', 'number', 'integer', 'boolean', 'array', 'object'], 'type': 'string', 'description': "The value's type."}, 'maximum': {'type': 'number', 'description': 'Upper bound for a numeric field.'}, 'minimum': {'type': 'number', 'description': 'Lower bound for a numeric field.'}, 'required': {'type': 'boolean', 'description': 'Defaults to true. Set false for a field that may be absent.'}, 'description': {'type': 'string', 'description': "Passed to the model as the field's description."}}}, 'description': 'The schema for 1 record.'}, 'selection': {'type': 'object', 'properties': {'maxCount': {'type': 'number'}, 'minCount': {'type': 'number'}, 'minScore': {'type': 'number'}, 'scoreField': {'type': 'string'}}, 'description': 'Use when you want the model to over-generate candidates and Rigor to sort, threshold, and cap them before the count bounds are checked.'}, 'task_type': {'type': 'string', 'description': 'Your own label for the work. Echoed into telemetry. Not read as a framework name and does not change routing.'}}, 'description': 'Only read when execution is "direct". Declares the JSON shape you want back, so the answer is generated against your schema and validated against it before return, instead of returned as prose you have to parse. A conforming run also skips the quality-review call, costing 1 LLM call rather than 2. The schema is closed: a record carrying an undeclared key is rejected exactly like one missing a required key.'}, 'skip_frameworks': {'type': 'array', 'items': {'type': 'string'}, 'description': 'Framework names to exclude from the plan.'}, 'require_approval': {'type': 'boolean', 'description': 'Pause at pending_approval before the final step.'}, 'approval_before_step': {'type': 'array', 'items': {'type': 'number'}, 'description': 'Zero-based step indices where approval gates are inserted.'}}, 'description': 'Optional workflow preferences.'}, 'task_description': {'type': 'string', 'description': "Natural language description of the task. Be specific — include what you want produced, constraints, and context. Example: 'Design a caching layer for our API gateway with Redis integration.'"}}, 'additionalProperties': False}
Output schema
{'type': 'object', 'properties': {'ok': {'type': 'boolean'}, 'plan': {'type': 'object', 'properties': {'cost': {'type': 'object', 'properties': {'usd': {'type': 'number'}, 'range': {'type': 'object'}, 'credits': {'type': 'number'}}}, 'info': {'type': 'array', 'items': {'type': 'string'}}, 'sequence': {'type': 'array', 'items': {'type': 'object', 'properties': {'name': {'type': 'string'}, 'step': {'type': 'number'}, 'estimated_credits': {'type': 'number'}}}}, 'warnings': {'type': 'array', 'items': {'type': 'object', 'properties': {'code': {'type': 'string'}, 'message': {'type': 'string'}, 'suggestion': {'type': 'string'}}}}, 'rigor_level': {'type': 'string'}, 'workflow_id': {'type': 'string'}, 'alternatives': {'type': 'object'}, 'allowed_modes': {'type': 'array', 'items': {'type': 'string'}, 'description': "Execution modes available for this plan beyond the standard multi-call default. Contains 'direct' when the plan is eligible for direct execution; empty array when it is not."}, 'classification': {'type': 'object', 'properties': {'task_type': {'type': 'string'}, 'value_class': {'type': 'string'}}}}}, 'generated_title': {'type': 'string'}}}
rigor_status
Check the status of a running or completed Rigor workflow. Returns progress, step results, and the full deliverable when complete. Use after rigor_execute with polling delivery to retrieve results.
Read only
Idempotent
Input schema
{'type': 'object', 'required': ['workflow_id'], 'properties': {'workflow_id': {'type': 'string', 'description': 'The workflow ID returned by rigor_execute (format: wr_xxx).'}}, 'additionalProperties': False}
Output schema
{'type': 'object', 'properties': {'ok': {'type': 'boolean'}, 'workflow': {'type': 'object', 'properties': {'error': {'type': 'string'}, 'status': {'type': 'string'}, 'task_type': {'type': 'string'}, 'created_at': {'type': 'string'}, 'deliverable': {'type': 'object', 'properties': {'title': {'type': 'string'}, 'sections': {'type': 'array', 'items': {'type': 'object', 'properties': {'content': {'type': 'string'}, 'heading': {'type': 'string'}}}}}}, 'total_steps': {'type': 'number'}, 'workflow_id': {'type': 'string'}, 'completed_at': {'type': 'string'}, 'credits_used': {'type': 'number'}, 'current_step': {'type': 'number'}, 'quality_review': {'type': 'object', 'properties': {'score': {'type': 'number'}, 'summary': {'type': 'string'}, 'path_to_100': {'type': 'array', 'items': {'type': 'string'}}}}, 'generated_title': {'type': 'string'}, 'actual_steps_executed': {'type': 'number'}, 'completed_step_summaries': {'type': 'array', 'items': {'type': 'object', 'properties': {'step': {'type': 'number'}, 'tool': {'type': 'string'}, 'model': {'type': 'string'}, 'summary': {'type': 'string'}, 'framework': {'type': 'string'}}}}}}}}
rigor_workflows
List and search Rigor workflows for your organization, with filtering and pagination. Returns status, progress, capacity usage, and available actions per workflow. Use to monitor workflow state, understand concurrent limit usage, identify stuck or completed workflows, and — via q — find prior work on a subject before commissioning it again. Pair a q hit with rigor_status to read that workflow's deliverable.
Read only
Idempotent
Input schema
{'type': 'object', 'required': [], 'properties': {'q': {'type': 'string', 'description': 'Search the workflow title and task description. Every whitespace-separated term must appear in one or the other, as a case-insensitive substring — so "vector search postgres" matches a task described as "add vector search to an existing Postgres-backed SaaS app". Substring matching, not full-text: there is no stemming and no ranking, so "migrating" does not match "migration". Max 200 characters and 8 terms; punctuation is treated as a separator and wildcards are not supported.'}, 'limit': {'type': 'number', 'description': 'Page size (default 20, max 100)'}, 'cursor': {'type': 'string', 'description': 'Pagination cursor (created_at timestamp from previous page)'}, 'status': {'type': 'string', 'description': 'Filter by status (comma-separated). Valid values: executing, step_executing, completed, failed, halted, pending_approval, cancelled. E.g. "halted,failed,pending_approval"'}, 'folder_id': {'type': 'string', 'description': 'Filter by folder ID. Pass "unassigned" for workflows in no folder'}, 'task_type': {'type': 'string', 'description': 'Filter by classified task type'}, 'created_after': {'type': 'string', 'description': 'ISO timestamp — only workflows created after this time'}, 'created_before': {'type': 'string', 'description': 'ISO timestamp — only workflows created before this time'}, 'counts_toward_limit': {'enum': ['true', 'false'], 'type': 'string', 'description': 'Filter to workflows counting toward the concurrent limit'}}, 'additionalProperties': False}
Output schema
{'type': 'object', 'properties': {'ok': {'type': 'boolean'}, 'workflows': {'type': 'array', 'items': {'type': 'object', 'properties': {'status': {'type': 'string'}, 'task_type': {'type': 'string'}, 'created_at': {'type': 'string'}, 'started_at': {'type': 'string'}, 'total_steps': {'type': 'number'}, 'workflow_id': {'type': 'string'}, 'completed_at': {'type': 'string'}, 'current_step': {'type': 'number'}, 'task_excerpt': {'type': 'string'}, 'history_locked': {'type': 'boolean'}, 'credits_charged': {'type': 'number'}, 'generated_title': {'type': 'string'}, 'available_actions': {'type': 'array', 'items': {'type': 'string'}}, 'counts_toward_limit': {'type': 'boolean'}}}}, 'pagination': {'type': 'object', 'properties': {'limit': {'type': 'number'}, 'cursor': {'type': 'string'}, 'has_more': {'type': 'boolean'}, 'total_count': {'type': 'number'}}}, 'credits_remaining': {'type': 'number'}, 'concurrent_summary': {'type': 'object', 'properties': {'limit': {'type': 'number'}, 'active': {'type': 'number'}, 'remaining': {'type': 'number'}}}}}
History
Recent tool changes
Added
rigor_workflows
Sept. 17, 2026, 7:57 a.m.
Added
rigor_status
Sept. 17, 2026, 7:57 a.m.
Added
rigor_execute
Sept. 17, 2026, 7:57 a.m.
Added
rigor_plan
Sept. 17, 2026, 7:57 a.m.
Added
pitfalldb_report
Sept. 17, 2026, 7:57 a.m.
Added
pitfalldb_query
Sept. 17, 2026, 7:57 a.m.
Added
guardrail_create_policy
Sept. 17, 2026, 7:57 a.m.
Added
guardrail_check
Sept. 17, 2026, 7:57 a.m.
Added
qualitygate_validate
Sept. 17, 2026, 7:57 a.m.
Added
burnrate_budget
Sept. 17, 2026, 7:57 a.m.
Added
burnrate_optimize
Sept. 17, 2026, 7:57 a.m.
Added
burnrate_track
Sept. 17, 2026, 7:57 a.m.
Added
burnrate_estimate
Sept. 17, 2026, 7:57 a.m.
Added
dedupq_complete
Sept. 17, 2026, 7:57 a.m.
Added
dedupq_check
Sept. 17, 2026, 7:57 a.m.
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