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Accepts a coupling matrix (covariance, correlation, or precision) and returns zone classifications, optimal factorization strategy, and calibrated risk predicti
Accepts a coupling matrix (covariance, correlation, or precision) and returns zone classifications, optimal factorization strategy, and calibrated risk predictions. Answers: which variable dependencies are load-bearing and which can be safely ignored?
Decision intelligence for AI agents. Send a coupling matrix — get zone classifications, optimal factorization strategy, and calibrated risk predictions.
Every system with interacting variables has a coupling structure. When an agent simplifies that system by treating variables as independent, it pays an information cost. FactorGuide quantifies that cost exactly.
FactorGuide is listed on the Official MCP Registry. Any MCP-compatible agent discovers tools automatically:
mcp_endpoint: https://factorguide.io/mcp
POST https://factorguide.io/navigate
POST https://factorguide.io/diagnose
POST https://factorguide.io/explain
POST https://factorguide.io/report_outcome
GET https://factorguide.io/llms.txt
GET https://factorguide.io/openapi.json
Send a 3×3 correlation matrix:
{
"coupling": {
"covariance_matrix": [
[1.00, 0.72, 0.05],
[0.72, 1.00, 0.48],
[0.05, 0.48, 1.00]
]
},
"sample_size": 500,
"model_class": "constitutive"
}
Get back zone classifications:
| Pair | IC | Zone | Recommendation |
|---|---|---|---|
| z0–z1 | 0.682 | 3 | PRESERVE |
| z0–z2 | 0.031 | 1 | FACTORIZE |
| z1–z2 | 0.453 | 2 | ASSESS |
| Tier | Price | Queries | Max Variables |
|---|---|---|---|
| Trial | Free | 15 per wallet | n ≤ 25 |
| Starter | $0.05/query | 50–200 bundles | n ≤ 100 |
| Professional | $0.03/query | 500–2000 bundles | n ≤ 1000 |
Payment: USDC on Base via x402 or MPP. report_outcome is always free.
FactorGuide accepts only second-order summary statistics — covariance, precision, correlation matrices, or edge lists. No raw observations. Matrices are zeroed after IC computation.
Built on Circulatory Fidelity, a mathematical framework where IC (Inference Coupling) measures the partial correlation between variables. The cost function V(IC) gives the exact mutual information destroyed by severing a coupling. Risk curves are calibrated on 49,000+ validated datapoints.
Every prediction is falsifiable. When agents report outcomes, the flywheel refines future predictions.
Built by CF Laboratory
Добавь это в claude_desktop_config.json и перезапусти Claude Desktop.
{
"mcpServers": {
"factorguide": {
"command": "npx",
"args": []
}
}
}