此 MCP 可以做什么
Analyzes model coupling structures and time-series regimes to recommend factorization strategies and estimate associated risks.
功能
工具
7
工具
factorguide_diagnose
Quick single-pair diagnostic. IC with risk prediction for both model classes. Include variances for sign detectability. Requires X-Wallet header with your EVM wallet address (0x...). First 5 queries are free trial.
输入模式
{'type': 'object', 'required': ['i', 'j', 'coupling_value', 'sample_size'], 'properties': {'i': {'type': 'string', 'description': 'First variable name'}, 'j': {'type': 'string', 'description': 'Second variable name'}, 'variance_i': {'type': 'number'}, 'variance_j': {'type': 'number'}, 'sample_size': {'type': 'integer', 'minimum': 10}, 'coupling_value': {'type': 'number', 'description': 'IC or coupling value'}}}
factorguide_explain
Plain-language explanation of a previous navigate response, including wave mechanics grounding for observational cost guidance. Requires a prediction_hash from a prior factorguide_navigate call. Consumes 1 query allocation. Available for starter and professional tiers. Requires X-Wallet header with your EVM wallet address (0x...). First 5 queries are free trial.
输入模式
{'type': 'object', 'required': ['prediction_hash'], 'properties': {'prediction_hash': {'type': 'string', 'description': 'prediction_hash from a previous navigate response'}}}
factorguide_navigate
Map the factorization terrain of your model. Send coupling structure (precision matrix preferred for n>2; covariance matrix recommended if sign or CC information is needed) and receive a block-diagonal strategy with calibrated risk prediction. Answers: 'How should I factorize, and what will it cost me?' Optional: set report_sign_detectability=true to get sign(ρ) for high-leverage pairs at no additional cost when variance ratio > 20. Requires X-Wallet header with your EVM wallet address (0x...). First 5 queries are free trial.
输入模式
{'type': 'object', '$defs': {'TaskType': {'enum': ['inference', 'control'], 'type': 'string', 'title': 'TaskType'}, 'CostModel': {'enum': ['cubic', 'quadratic', 'linear', 'information_cost'], 'type': 'string', 'title': 'CostModel'}, 'ModelClass': {'enum': ['filtering', 'hierarchical', 'deep_hierarchy', 'graphical_model', 'gp', 'vae', 'unknown', 'constitutive', 'inductive'], 'type': 'string', 'title': 'ModelClass'}, 'EdgeListInput': {'type': 'object', 'title': 'EdgeListInput', 'required': ['edge_list', 'n'], 'properties': {'n': {'type': 'integer', 'title': 'N', 'minimum': 2}, 'edge_list': {'type': 'array', 'items': {}, 'title': 'Edge List'}}}, 'PrecisionMatrixInput': {'type': 'object', 'title': 'PrecisionMatrixInput', 'required': ['precision_matrix'], 'properties': {'precision_matrix': {'type': 'array', 'items': {'type': 'array', 'items': {'type': 'number'}}, 'title': 'Precision Matrix'}}}, 'CovarianceMatrixInput': {'type': 'object', 'title': 'CovarianceMatrixInput', 'required': ['covariance_matrix'], 'properties': {'covariance_matrix': {'type': 'array', 'items': {'type': 'array', 'items': {'type': 'number'}}, 'title': 'Covariance Matrix'}}}, 'CorrelationMatrixInput': {'type': 'object', 'title': 'CorrelationMatrixInput', 'required': ['correlation_matrix'], 'properties': {'correlation_matrix': {'type': 'array', 'items': {'type': 'array', 'items': {'type': 'number'}}, 'title': 'Correlation Matrix'}}}, 'DistributionDiagnostics': {'type': 'object', 'title': 'DistributionDiagnostics', 'properties': {'skewness': {'anyOf': [{'type': 'array', 'items': {'type': 'number'}}, {'type': 'null'}], 'title': 'Skewness', 'default': None}, 'excess_kurtosis': {'anyOf': [{'type': 'array', 'items': {'type': 'number'}}, {'type': 'null'}], 'title': 'Excess Kurtosis', 'default': None}, 'spearman_rank_correlation': {'anyOf': [{'type': 'array', 'items': {'type': 'array', 'items': {'type': 'number'}}}, {'type': 'null'}], 'title': 'Spearman Rank Correlation', 'default': None}}}}, 'title': 'NavigateRequest', 'required': ['coupling', 'sample_size'], 'properties': {'coupling': {'anyOf': [{'$ref': '#/$defs/PrecisionMatrixInput'}, {'$ref': '#/$defs/CorrelationMatrixInput'}, {'$ref': '#/$defs/CovarianceMatrixInput'}, {'$ref': '#/$defs/EdgeListInput'}], 'title': 'Coupling'}, 'task_type': {'$ref': '#/$defs/TaskType', 'default': 'inference'}, 'cost_model': {'$ref': '#/$defs/CostModel', 'default': 'cubic'}, 'model_class': {'$ref': '#/$defs/ModelClass', 'default': 'unknown'}, 'sample_size': {'type': 'integer', 'title': 'Sample Size', 'minimum': 10}, 'synergy_check': {'type': 'boolean', 'title': 'Synergy Check', 'default': False}, 'compute_budget': {'anyOf': [{'type': 'number'}, {'type': 'string', 'const': 'minimize'}], 'title': 'Compute Budget', 'default': 'minimize'}, 'encoding_label': {'anyOf': [{'type': 'string', 'maxLength': 128}, {'type': 'null'}], 'title': 'Encoding Label', 'default': None}, 'variable_names': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'Variable Names', 'default': None}, 'accuracy_target': {'type': 'number', 'title': 'Accuracy Target', 'default': 2.0, 'maximum': 7.0, 'exclusiveMinimum': 1.0}, 'report_marginal_ic': {'type': 'boolean', 'title': 'Report Marginal Ic', 'default': False}, 'distribution_diagnostics': {'anyOf': [{'$ref': '#/$defs/DistributionDiagnostics'}, {'type': 'null'}], 'default': None}, 'report_sign_detectability': {'type': 'boolean', 'title': 'Report Sign Detectability', 'default': False}}}
factorguide_regime_detect
Detect coupling regime changes in time series via windowed IC. Specification pending — v1.1 target.
输入模式
{'type': 'object', 'properties': {}}
factorguide_report_outcome
Complete the prediction loop — report inference diagnostics so future predictions improve. After running the approach FactorGuide recommended, return your ESS ratio, PSIS-khat, or log-likelihood gap. Zero additional computation required. Does not consume a query allocation.
输入模式
{'type': 'object', '$defs': {'ApproachTaken': {'enum': ['factorized', 'structured', 'hybrid'], 'type': 'string', 'title': 'ApproachTaken'}}, 'title': 'OutcomeReport', 'required': ['prediction_hash', 'approach_taken'], 'properties': {'ess_ratio': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Ess Ratio', 'default': None}, 'psis_khat': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Psis Khat', 'default': None}, 'log_lik_gap': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Log Lik Gap', 'default': None}, 'approach_taken': {'$ref': '#/$defs/ApproachTaken'}, 'n_replications': {'anyOf': [{'type': 'integer'}, {'type': 'null'}], 'title': 'N Replications', 'default': None}, 'prediction_hash': {'type': 'string', 'title': 'Prediction Hash'}, 'runtime_seconds': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Runtime Seconds', 'default': None}, 'actual_mse_ratio': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Actual Mse Ratio', 'default': None}}}
factorguide_submit_payment
Submit payment proof after sending stablecoins to a FactorGuide wallet address. For x402: provide tx_hash and chain. For MPP: use in-band Authorization header instead — no separate submission needed.
输入模式
{'type': 'object', 'required': ['tx_hash', 'chain'], 'properties': {'chain': {'type': 'string', 'description': "Chain identifier, e.g. 'eip155:8453' or 'tempo:4217'"}, 'tx_hash': {'type': 'string', 'description': 'On-chain transaction hash'}}}
factorguide_synergy_detect
Detect hidden synergistic structure via Walsh-Hadamard spectral analysis. Accepts pre-computed Walsh coefficients — agent performs the transform locally and sends only the spectral summary. Specification pending — v1.1 target.
输入模式
{'type': 'object', 'properties': {'n_samples': {'type': ['integer', 'null']}, 'n_variables': {'type': 'integer'}, 'ic_matrix_ref': {'type': 'string'}, 'transform_method': {'enum': ['exact', 'sampled'], 'type': 'string'}, 'walsh_coefficients': {'type': 'object', 'properties': {'order_0': {'type': 'number'}, 'order_1': {'type': 'array', 'items': {'type': 'number'}}, 'order_2': {'type': 'array', 'items': {'type': 'object', 'properties': {'pair': {'type': 'array', 'items': {'type': 'integer'}}, 'coefficient': {'type': 'number'}}}}, 'order_3': {'type': 'array', 'items': {'type': 'object', 'properties': {'triple': {'type': 'array', 'items': {'type': 'integer'}}, 'coefficient': {'type': 'number'}}}}}}}}
历史
近期工具变更
已添加
factorguide_synergy_detect
2026年9月17日 12:40
已添加
factorguide_regime_detect
2026年9月17日 12:40
已添加
factorguide_explain
2026年9月17日 12:40
已添加
factorguide_report_outcome
2026年9月17日 12:40
已添加
factorguide_submit_payment
2026年9月17日 12:40
已添加
factorguide_diagnose
2026年9月17日 12:40
已添加
factorguide_navigate
2026年9月17日 12:40
发现
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