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pysr_run

Read-only

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 $0.35 per-transaction fee (Stripe
processing), so the MPP challenge amount in a `payment_required`
response is $0.35 higher than the x402 amount for the same base
price; x402 gets the lower rate. Omit `payment` for free-tier
requests; paid requests without a valid credential receive a
`payment_required` result with pricing and accepted schemes. Full
pricing: occam://pricing

Advisory limits: jobs over 50,000 rows or 20 features are accepted
but may not converge; response carries a top-level `warning`.

Operators: fixed supported set only — custom operators (e.g.
'inv(x) = 1/x') are rejected. Unary: sin, cos, tan, exp, log, log2,
log10, sqrt, abs, sinh, cosh, tanh. Binary: +, -, *, /, ^.
See also prompt `supported_operators`.

Loss metric: `loss` (in `pareto_front[].loss` and `best_loss`) is
mean squared error between model prediction and `y` on the full
training set — not RMSE, and not normalized by Var(y). A threshold
appropriate for one dataset scales with y's magnitude, so set
`loss_threshold` with that in mind (e.g. for y values near 1.0,
1e-6 is a tight fit; for y near 1000, the equivalent is 1.0).

Early termination: set `loss_threshold` to stop at your noise floor.
The server also stops when the search stalls (<1% improvement in the
last third of the budget); disable with `stall_detection=false`.
Response `stop_reason` is one of: loss_threshold, stall, timeout,
natural.

If `feature_names` is supplied, its length must equal the number of
columns in `X`; a mismatch is rejected with a validation error.

Follow-up: call `pysr_uncertainty` with a chosen expression and the
same dataset for bootstrap confidence intervals on its fit constants
and optional prediction bands.

Rate limit: 10 requests/hour per IP, 200/hour global, max queue
depth 20 (shared with sindy_run and pysr_uncertainty).

Response (success) includes `pareto_front[]` (each with `complexity`,
`loss`, `expression`, `expression_latex`), `best_expression`,
`best_expression_latex`, `best_loss`, `best_complexity`, `stop_reason`,
`elapsed_seconds`, `queue_seconds` (>0 = server saturated; use as
backoff signal), optional `warning`, optional `_meta` (MPP receipt).
Full response and payment-required schemas: occam://tool-schemas

Example request:
  X=[[0.0], [1.0], [2.0], [3.0]], y=[1.0, 3.0, 5.0, 7.0],
  feature_names=["x"], max_complexity=10, timeout_seconds=15

Policy: occam://privacy-policy — Citation: occam://citation-info

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
XYes2D array of input features. Each row is an observation, each column is a feature. Free tier: 100 rows, 8 features. Paid tier: up to 50,000 rows, 20 features.
yYesTarget values, one per row of X.
paymentNoPayment credential. Accepts either a JSON object or a JSON-encoded string (FastMCP's transport pre-parses strings whose field annotation is non-bare-`str` into objects, so the object form is canonical; the string form is accepted for legacy callers). Required when the dataset exceeds the free tier (100 rows, 8 variables). Omit for free-tier requests. For x402: {"transaction":"0x...","network":"...","priceToken":"..."}. For MPP/Stripe: {"challenge":{...},"payload":"..."}. For prepaid API key: {"scheme":"prepaid","api_key":"occ_live_...","request_id":"<optional uuid>"}.
populationsNoNumber of evolutionary populations for the search. Default 15, max 20.
feature_namesNoNames for each variable/feature column. Defaults to x0, x1, ...
loss_thresholdNoOptional early-stop threshold on the best loss found. If set, the search terminates as soon as any Pareto-front member reaches a loss at or below this value, even if the timeout has not been reached. Useful when you know your noise floor. Default: None (no user threshold; the search runs until the stall detector or timeout).
max_complexityNoMaximum expression tree size. Higher allows more complex expressions. Default 20, max 25.
stall_detectionNoWhen true (default), the server stops the search early if the best loss has not improved by more than 1% during the last third of the time budget. This reclaims compute once the search has converged. Set to false only if you want the search to run for the full timeout regardless of progress.
timeout_secondsNoWall clock time limit in seconds. Free tier: max 60. Paid tier: max 300 (5 minutes). Default 60.
unary_operatorsNoAllowed unary operators, drawn from the fixed supported set: sin, cos, tan, exp, log, log2, log10, sqrt, abs, sinh, cosh, tanh. Custom operators (e.g. 'inv(x) = 1/x') are NOT supported — only the names listed are accepted. Default: sin, cos, exp, log, sqrt. Pass [] for none.
binary_operatorsNoAllowed binary operators, drawn from the fixed supported set: +, -, *, /, ^. Custom operators are NOT supported. Default: +, -, *, /. Pass [] for none.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
warningNo
best_lossNo
stop_reasonNo
pareto_frontNo
queue_secondsNo
best_complexityNo
best_expressionNo
elapsed_secondsNo
best_expression_latexNo

TDQS

A5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations declare readOnlyHint=true and destructiveHint=false, and the description adds substantial behavioral context beyond that: early-stall termination with stop_reason values, loss metric definition (MSE not RMSE), feature_names length validation, server saturation signals (queue_seconds), and payment-related response behavior. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but highly structured with clear sections (purpose, pricing, limits, operators, loss, early termination, follow-up, rate limits, response). It opens with the core purpose and front-loads critical constraints. Each paragraph serves a distinct function, and no content is redundant or promotional. For an 11-parameter tool with complex pricing and operational behavior, this length is appropriate.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (11 parameters, multi-tier pricing, payment schemes, rate limits, large-job warnings, output structures), the description is exhaustive. It covers success response fields, payment_required schema references, example request, and points to full schemas. It anticipates edge cases like feature_names length mismatch and non-convergence for large inputs, making it complete for an agent to select and invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although schema coverage is 100%, the description adds meaningful semantics: explains how to interpret loss_threshold across different y magnitudes, details stall_detection behavior, describes the payment parameter formats and omission for free tier, and clarifies operator restrictions (custom operators rejected). This value exceeds what the schema alone provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Discovers algebraic equations y = f(x1, x2, ...) from feature/target data.' It clearly distinguishes from siblings by explicitly directing differential-equation tasks to sindy_run and mentions pysr_uncertainty as a follow-up. This leaves no ambiguity about what the tool does and how it differs from alternatives.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit when-to-use guidance ('for differential equations from time series, use sindy_run instead'), defines free vs paid tier constraints, and states rate limits (10/hour per IP, 200/hour global). It also clarifies when to omit payment and what happens with paid requests without valid credentials, giving the agent full decision support.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.7/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: feature_request handles capability requests, pysr_run does algebraic symbolic regression, sindy_run handles differential equation discovery, and pysr_uncertainty provides bootstrap confidence intervals for pysr results. The descriptions explicitly cross-reference each other to prevent confusion.

Naming Consistency4/5

The compute tools follow a consistent 'method_run' pattern (pysr_run, sindy_run), and pysr_uncertainty shares the pysr prefix. feature_request deviates from this pattern but is a distinctly different kind of tool, so the naming is largely predictable with only minor inconsistency.

Tool Count5/5

Four tools is a well-scoped set for a symbolic regression server. Each tool fills a necessary role: two core methods, one uncertainty analysis follow-up, and one feedback channel. No redundancy or bloat.

Completeness4/5

The domain covers algebraic regression, dynamical system identification, and post-hoc uncertainty quantification for pysr. A minor gap is the lack of a sindy_uncertainty tool, but the feature_request mechanism compensates for missing capabilities, and the core workflows are fully supported.

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