Occam
Finds the simplest equation consistent with your data. SINDy and PySR symbolic regression via MCP.
Connect it
Settings → Connectors → Add custom connector
URL: https://occam.fit/mcp/Settings → Apps & Connectors → Advanced → Developer mode
Create connector with URL: https://occam.fit/mcp/{
"mcpServers": {
"occam": {
"url": "https://occam.fit/mcp/"
}
}
}claude mcp add --transport http occam https://occam.fit/mcp/{
"servers": {
"occam": {
"type": "http",
"url": "https://occam.fit/mcp/"
}
}
}4 tools, verified live
feature_requestRequest 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. Descriptions longer than 500 characters are truncated.
sindy_runread-onlySparse 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 100 rows and 8 variables. Beyond that, $0.05 + $0.01 per 100 extra rows +
pysr_runread-onlyEvolutionary 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 series, use sindy_run instead. Pricing: free tier up to 100 rows × 8 features,
pysr_uncertaintyread-onlyBootstrap 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 constants. Returns frequentist bootstrap confidence intervals, not Bayesian c
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