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factorguide

factorguide

Remote
github.com/Bwana7AI & Agents0Updated 29w ago
Repository

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

  1. 01Classify coupling zones
  2. 02Recommend factorization strategies
  3. 03Generate calibrated risk predictions
  4. 04Accept covariance, precision, and correlation matrices
  5. 05Use reported outcomes to refine predictions

Use cases

  1. 01Determine whether variables can be safely factorized
  2. 02Identify relationships that should be preserved
  3. 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.

Endpointhttps://factorguide.io/mcp
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

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
ParameterTypeRequiredDescription
couplinganyYes
task_typeanyNo
cost_modelanyNo
model_classanyNo
sample_sizeintegerYes
synergy_checkbooleanNo
compute_budgetanyNo
encoding_labelanyNo
variable_namesanyNo
accuracy_targetnumberNo
report_marginal_icbooleanNo
distribution_diagnosticsanyNo
report_sign_detectabilitybooleanNo

Details

From the project 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:

PairICZoneRecommendation
z0–z10.6823PRESERVE
z0–z20.0311FACTORIZE
z1–z20.4532ASSESS
  • 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

TierPriceQueriesMax Variables
TrialFree15 per walletn ≤ 25
Starter$0.05/query50–200 bundlesn ≤ 100
Professional$0.03/query500–2000 bundlesn ≤ 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


Built by CF Laboratory

Comments

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New to MCP?

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.