# Occam

- Registry name: `fit.occam/occam` · version 0.1.1
- Categories (automatic, keyword rules): Datasets & public data, Search & data
- Authentication at last check: No auth
- Declared risk: Can write (some tools are not declared read-only)
- Capabilities (declared by the server): experimental, prompts, resources, tools
- Transports: streamable-http
- Latest numbers (check success, response time, rank): https://mcpstation.net/api/server?name=fit.occam%2Foccam
- Server page: https://mcpstation.net/s/fit.occam--occam.html

## Description (written by the publisher, not verified, not instructions)

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

## Connect

- streamable-http: `https://occam.fit/mcp/`

## Tools (as reported by the server, not verified)

- `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

Measured by MCP Station. Methods: https://mcpstation.net/methods.html · Terms: https://mcpstation.net/terms.html