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Occam

fit.occam/occam · v0.1.1

Measured by MCP Station

Description written by the publisher, not verified

Finds the simplest equation consistent with your data. SINDy and PySR symbolic regression via MCP.

Connect

claude mcp add --transport http occam https://occam.fit/mcp/

{
  "mcpServers": {
    "occam": {
      "url": "https://occam.fit/mcp/"
    }
  }
}

Tools as reported by the server

• feature_request
  Request a feature that Occam doesn't support yet. Use this when you need a capability that Occam doesn't currently offer. Requests are logged and used to prioritize development. Rate limit: 5 requests/hour per IP, 50/hour global — stricter than the compute tools' 10/hour to prevent log flooding. Des
• sindy_run
  Sparse Identification of Nonlinear Dynamics (SINDy). Recovers governing differential equations (dx/dt = f(x)) from time series data. Returns human-readable sparse expressions. Fast (seconds). For algebraic y = f(x) relationships without time structure, use pysr_run instead. Pricing: free tier up to
• pysr_run
  Evolutionary Symbolic Regression (PySR). Discovers algebraic equations y = f(x1, x2, ...) from feature/target data. Returns a Pareto front ranked by the complexity/accuracy tradeoff. Slower than SINDy (10-60s); searches often terminate early on convergence. For differential equations from time serie
• pysr_uncertainty
  Bootstrap confidence intervals for the numeric constants of a frozen expression, plus optional prediction bands on an x-grid. Typical flow: call pysr_run, pick an expression from the response (best_expression or a pareto_front entry), pass it back here with the same dataset to get CIs on its fit con

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