Occam
Finds the simplest equation consistent with your data. SINDy and PySR symbolic regression via MCP.
| Endpoint | https://occam.fit/mcp/ (streamable-http) |
|---|---|
| Registry version | 0.1.1 |
| Now | 4 tools · grade F (45) · pin ts_b21ea187ad5837e35ea7aa42c562ed31 · protocol 2025-06-18 |
| First / last listed | 2026-10-25 06:56 UTC / 2026-10-25 06:56 UTC |
| Grade over time | 2026-10-25: F (45), 4 tools |
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Tools: feature_request sindy_run pysr_run pysr_uncertainty
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.
params: description* |
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 + params: data*, t*, feature_names, poly_degree, threshold, max_iter, payment |
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, params: X*, y*, feature_names, unary_operators, binary_operators, max_complexity, populations, timeout_seconds, loss_threshold, stall_detection, payment |
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
c params: expression*, X*, y*, feature_names, y_sigma, n_resamples, alpha, x_grid |
Entries describe changes in tool definitions. They are not statements about a server, its quality or its author. The grade measures how the tool definitions fit model providers (token cost, schema compatibility, confusable names) as scored by the ToolFit engine at the time of the listing. JSON: append .json. Opt-out: how or hello@youraitools.co.