Occam — Symbolic Regression as a Service

# SR-MCP > Symbolic regression as a service via Model Context Protocol (MCP) SR-MCP exposes two symbolic regression tools to MCP-compatible AI clients: ## Tools ### sindy_run Sparse Identification of Nonlinear Dynamics. Discovers differential equations from time series data. - Input: time series array + timestamps - Output: sparse governing equations as human-readable expressions - Speed: seconds - Free tier: 100 rows, 8 variables (no payment needed) - Paid tier: up to 500,000 rows, 50 variables - Advisory: jobs exceeding 500,000 rows or 50 variables will be accepted but are unlikely to converge within the time budget ### pysr_run Evolutionary symbolic regression via SymbolicRegression.jl. Supported operators (fixed set; custom operators NOT supported): - Unary: sin, cos, tan, exp, log, log2, log10, sqrt, abs, sinh, cosh, tanh - Binary: +, -, *, /, ^ - Input: feature matrix X + target vector y - Output: Pareto front of expressions (complexity vs accuracy tradeoff), plus a `stop_reason` field ("loss_threshold", "stall", "timeout", or "natural") - Speed: 10-60 seconds; often less when the search converges early - Optional `loss_threshold`: stop once best loss ≤ this value (useful if you know your noise floor) - Optional `stall_detection` (default true): stop if best loss has not improved by more than 1% during the last third of the timeout budget - Free tier: 100 rows, 8 features, 60s timeout - Paid tier: up to 50,000 rows, 20 features, 300s (5 min) timeout - Advisory: jobs exceeding 50,000 rows or 20 features will be accepted but are unlikely to converge within the time budget ### pysr_uncertainty Bootstrap confidence intervals for the numeric constants of a frozen expression returned by pysr_run, plus optional prediction bands. **Frequentist bootstrap CIs, not Bayesian credible intervals.** Posterior inference over expression structures is an open research problem — this tool fixes the expression chosen by the caller and bootstraps only its nu