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Send a coupling matrix, get zone classifications and optimal factorization strategy.
About
Send a coupling matrix, get zone classifications and optimal factorization strategy.
Remote endpoints: streamable-http: https://factorguide.io/mcp
Security Report
Valid MCP server (1 strong, 1 medium validity signals). No known CVEs in dependencies. Imported from the Official MCP Registry.
7 tools verified · Open access · 1 issue found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Permissions Required
This plugin requests these system permissions. Most are normal for its category.
How to Connect
Remote Plugin
No local installation needed. Your AI client connects to the remote endpoint directly.
Add this to your MCP configuration to connect:
{
"mcpServers": {
"io-github-bwana7-factorguide": {
"url": "https://factorguide.io/mcp"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
FactorGuide
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.
Quick Start
MCP (Model Context Protocol)
FactorGuide is listed on the Official MCP Registry. Any MCP-compatible agent discovers tools automatically:
mcp_endpoint: https://factorguide.io/mcp
HTTP REST
POST https://factorguide.io/navigate
POST https://factorguide.io/diagnose
POST https://factorguide.io/explain
POST https://factorguide.io/report_outcome
Agent Discovery
GET https://factorguide.io/llms.txt
GET https://factorguide.io/openapi.json
Example
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 |
- Zone 1: Safe to factorize. Coupling below threshold.
- Zone 2: Depends on functional role. Same IC, opposite recommendations for constitutive vs. inductive coupling.
- Zone 3: Must preserve. Coupling is load-bearing regardless of model class.
Pricing
| 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.
Data Privacy
FactorGuide accepts only second-order summary statistics — covariance, precision, correlation matrices, or edge lists. No raw observations. Matrices are zeroed after IC computation.
Theoretical Foundation
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.
Links
- Website: factorguide.io
- Theory: circulatoryfidelity.com
- MCP Registry: registry.modelcontextprotocol.io
Built by CF Laboratory
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