factorguide
Send a coupling matrix, get zone classifications and optimal factorization strategy.
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Calidad y seguridad
Basado en el análisis automatizado de las definiciones de herramientas y el cumplimiento del protocolo.
Costo de contexto
Este es el número aproximado de tokens que se consumen cada vez que las herramientas del servidor se cargan en el contexto de un modelo. Los recuentos más altos reducen la atención disponible para otras tareas.
Instalar
Instalación con un clic
Agrega esto a tu archivo `claude_desktop_config.json`:
{
"mcpServers": {
"factorguide": {
"url": "https://factorguide.io/mcp"
}
}
}Puntos de conexión remotos
https://factorguide.io/mcpstreamable-httpQué puede hacer
Inventario de herramientas
Herramientas (7)
🟡factorguide_navigate(coupling, sample_size, model_class, accuracy_target, compute_budget, ...)
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.
Esquema de entrada
{
"type": "object",
"properties": {
"coupling": {
"anyOf": [
{
"$ref": "#/$defs/PrecisionMatrixInput"
},
{
"$ref": "#/$defs/CorrelationMatrixInput"
},
{
"$ref": "#/$defs/CovarianceMatrixInput"
},
{
"$ref": "#/$defs/EdgeListInput"
}
],
"title": "Coupling"
},
"sample_size": {
"minimum": 10,
"title": "Sample Size",
"type": "integer"
},
"model_class": {
"$ref": "#/$defs/ModelClass",
"default": "unknown"
},
"accuracy_target": {
"default": 2,
"exclusiveMinimum": 1,
"maximum": 7,
"title": "Accuracy Target",
"type": "number"
},
"compute_budget": {
"anyOf": [
{
"type": "number"
},
{
"const": "minimize",
"type": "string"
}
],
"default": "minimize",
"title": "Compute Budget"
},
"cost_model": {
"$ref": "#/$defs/CostModel",
"default": "cubic"
},
"variable_names": {
"anyOf": [
{
"items": {
"type": "string"
},
"type": "array"
},
{
"type": "null"
}
],
"default": null,
"title": "Variable Names"
},
"synergy_check": {
"default": false,
"title": "Synergy Check",
"type": "boolean"
},
"report_marginal_ic": {
"default": false,
"title": "Report Marginal Ic",
"type": "boolean"
},
"report_sign_detectability": {
"default": false,
"title": "Report Sign Detectability",
"type": "boolean"
},
"task_type": {
"$ref": "#/$defs/TaskType",
"default": "inference"
},
"distribution_diagnostics": {
"anyOf": [
{
"$ref": "#/$defs/DistributionDiagnostics"
},
{
"type": "null"
}
],
"default": null
},
"encoding_label": {
"anyOf": [
{
"maxLength": 128,
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Encoding Label"
}
},
"required": [
"coupling",
"sample_size"
],
"$defs": {
"CorrelationMatrixInput": {
"properties": {
"correlation_matrix": {
"items": {
"items": {
"type": "number"
},
"type": "array"
},
"title": "Correlation Matrix",
"type": "array"
}
},
"required": [
"correlation_matrix"
],
"title": "CorrelationMatrixInput",
"type": "object"
},
"CostModel": {
"enum": [
"cubic",
"quadratic",
"linear",
"information_cost"
],
"title": "CostModel",
"type": "string"
},
"CovarianceMatrixInput": {
"properties": {
"covariance_matrix": {
"items": {
"items": {
"type": "number"
},
"type": "array"
},
"title": "Covariance Matrix",
"type": "array"
}
},
"required": [
"covariance_matrix"
],
"title": "CovarianceMatrixInput",
"type": "object"
},
"DistributionDiagnostics": {
"properties": {
"excess_kurtosis": {
"anyOf": [
{
"items": {
"type": "number"
},
"type": "array"
},
{
"type": "null"
}
],
"default": null,
"title": "Excess Kurtosis"
},
"skewness": {
"anyOf": [
{
"items": {
"type": "number"
},
"type": "array"
},
{
"type": "null"
}
],
"default": null,
"title": "Skewness"
},
"spearman_rank_correlation": {
"anyOf": [
{
"items": {
"items": {
"type": "number"
},
"type": "array"
},
"type": "array"
},
{
"type": "null"
}
],
"default": null,
"title": "Spearman Rank Correlation"
}
},
"title": "DistributionDiagnostics",
"type": "object"
},
"EdgeListInput": {
"properties": {
"edge_list": {
"items": {},
"title": "Edge List",
"type": "array"
},
"n": {
"minimum": 2,
"title": "N",
"type": "integer"
}
},
"required": [
"edge_list",
"n"
],
"title": "EdgeListInput",
"type": "object"
},
"ModelClass": {
"enum": [
"filtering",
"hierarchical",
"deep_hierarchy",
"graphical_model",
"gp",
"vae",
"unknown",
"constitutive",
"inductive"
],
"title": "ModelClass",
"type": "string"
},
"PrecisionMatrixInput": {
"properties": {
"precision_matrix": {
"items": {
"items": {
"type": "number"
},
"type": "array"
},
"title": "Precision Matrix",
"type": "array"
}
},
"required": [
"precision_matrix"
],
"title": "PrecisionMatrixInput",
"type": "object"
},
"TaskType": {
"enum": [
"inference",
"control"
],
"title": "TaskType",
"type": "string"
}
},
"title": "NavigateRequest"
}⚪factorguide_diagnose(i, j, coupling_value, sample_size, variance_i, ...)
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.
Esquema de entrada
{
"type": "object",
"properties": {
"i": {
"type": "string",
"description": "First variable name"
},
"j": {
"type": "string",
"description": "Second variable name"
},
"coupling_value": {
"type": "number",
"description": "IC or coupling value"
},
"sample_size": {
"type": "integer",
"minimum": 10
},
"variance_i": {
"type": "number"
},
"variance_j": {
"type": "number"
}
},
"required": [
"i",
"j",
"coupling_value",
"sample_size"
]
}🟡factorguide_submit_payment(tx_hash, chain)
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.
Esquema de entrada
{
"type": "object",
"properties": {
"tx_hash": {
"type": "string",
"description": "On-chain transaction hash"
},
"chain": {
"type": "string",
"description": "Chain identifier, e.g. 'eip155:8453' or 'tempo:4217'"
}
},
"required": [
"tx_hash",
"chain"
]
}🟢factorguide_report_outcome(prediction_hash, approach_taken, ess_ratio, psis_khat, log_lik_gap, ...)
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.
Esquema de entrada
{
"type": "object",
"properties": {
"prediction_hash": {
"title": "Prediction Hash",
"type": "string"
},
"approach_taken": {
"$ref": "#/$defs/ApproachTaken"
},
"ess_ratio": {
"anyOf": [
{
"type": "number"
},
{
"type": "null"
}
],
"default": null,
"title": "Ess Ratio"
},
"psis_khat": {
"anyOf": [
{
"type": "number"
},
{
"type": "null"
}
],
"default": null,
"title": "Psis Khat"
},
"log_lik_gap": {
"anyOf": [
{
"type": "number"
},
{
"type": "null"
}
],
"default": null,
"title": "Log Lik Gap"
},
"actual_mse_ratio": {
"anyOf": [
{
"type": "number"
},
{
"type": "null"
}
],
"default": null,
"title": "Actual Mse Ratio"
},
"n_replications": {
"anyOf": [
{
"type": "integer"
},
{
"type": "null"
}
],
"default": null,
"title": "N Replications"
},
"runtime_seconds": {
"anyOf": [
{
"type": "number"
},
{
"type": "null"
}
],
"default": null,
"title": "Runtime Seconds"
}
},
"required": [
"prediction_hash",
"approach_taken"
],
"$defs": {
"ApproachTaken": {
"enum": [
"factorized",
"structured",
"hybrid"
],
"title": "ApproachTaken",
"type": "string"
}
},
"title": "OutcomeReport"
}🟢factorguide_explain(prediction_hash)
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.
Esquema de entrada
{
"type": "object",
"properties": {
"prediction_hash": {
"type": "string",
"description": "prediction_hash from a previous navigate response"
}
},
"required": [
"prediction_hash"
]
}⚪factorguide_regime_detect
Detect coupling regime changes in time series via windowed IC. Specification pending — v1.1 target.
Esquema de entrada
{
"type": "object",
"properties": {}
}⚪factorguide_synergy_detect(walsh_coefficients, n_variables, transform_method, n_samples, ic_matrix_ref)
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.
Esquema de entrada
{
"type": "object",
"properties": {
"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"
}
}
}
}
}
},
"n_variables": {
"type": "integer"
},
"transform_method": {
"type": "string",
"enum": [
"exact",
"sampled"
]
},
"n_samples": {
"type": [
"integer",
"null"
]
},
"ic_matrix_ref": {
"type": "string"
}
}
}Comunidad
Evidencia