Occam

Where it sits in the directory's order

What can be seen from outside, weighed as the directory publishes.

Where it answers

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

The tools it lists (4, as of 2 days ago)

The tool-description rules found nothing in these descriptions. They look for instructions aimed at a model and for hidden characters; they cannot see what a tool does when it runs.

• 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. Descriptions longer than 500 characters are truncated.

• 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 100 rows and 8 variables. Beyond that, $0.05 + $0.01 per 100 extra rows + $0.01 per extra variable squared, via x402 (USDC on Base) or MPP/Stripe. MPP/Stripe adds a flat $0.35 per-transaction fee (Stripe processing), so the MPP challenge amount in a `payment_req

• 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 series, use sindy_run instead. Pricing: free tier up to 100 rows × 8 features, 60s timeout. Beyond that, $0.25 + $0.03 per 100 extra rows + $0.01 per extra feature squared, timeout up to 300s (5 min), via x402 (USDC on Base) or MPP/Stripe. MPP/Stripe adds a flat $0.3

• 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 constants. Returns frequentist bootstrap confidence intervals, not Bayesian credible intervals — posterior inference over expression structures is an open research problem. This tool freezes the expression chosen by the caller and bootstraps only its numeric constants;