About factorguide
FactorGuide serves AI agents simplifying systems with interacting variables. It uses coupling matrices to classify zones, recommend factorization strategies, and produce risk predictions, accepting second-order summaries rather than raw observations.
Key features
- 01Classify coupling zones
- 02Recommend factorization strategies
- 03Generate calibrated risk predictions
- 04Accept covariance, precision, and correlation matrices
- 05Use reported outcomes to refine predictions
Use cases
- 01Determine whether variables can be safely factorized
- 02Identify relationships that should be preserved
- 03Assess coupling risk before simplifying a system
MCP support
FactorGuide provides an MCP endpoint at https://factorguide.io/mcp, where compatible agents can discover and call its analysis tools.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://factorguide.io/mcpclaude mcp add --transport http io-factorguide-factorguide https://factorguide.io/mcp{"mcpServers":{"io-factorguide-factorguide":{"type":"http","url":"https://factorguide.io/mcp"}}}Open in Cursor{"servers":{"io-factorguide-factorguide":{"type":"http","url":"https://factorguide.io/mcp"}}}Open in VS Code{"mcpServers":{"io-factorguide-factorguide":{"type":"http","url":"https://factorguide.io/mcp"}}}{"mcpServers":{"io-factorguide-factorguide":{"serverUrl":"https://factorguide.io/mcp"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
Tools
7| Parameter | Type | Required | Description |
|---|---|---|---|
| coupling | any | Yes | |
| task_type | any | No | |
| cost_model | any | No | |
| model_class | any | No | |
| sample_size | integer | Yes | |
| synergy_check | boolean | No | |
| compute_budget | any | No | |
| encoding_label | any | No | |
| variable_names | any | No | |
| accuracy_target | number | No | |
| report_marginal_ic | boolean | No | |
| distribution_diagnostics | any | No | |
| report_sign_detectability | boolean | No |
Details
From the project READMEFactorGuide
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
Comments
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From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.