A
license-
qualityA
maintenanceProvides a token-efficient exact math engine for AI agents, enabling computation of derivatives, integrals, equations, and optimized Python/NumPy code via a single MCP tool.
4
MIT
Occam
by fit.occam
Server Details
Finds the simplest equation consistent with your data. SINDy and PySR symbolic regression via MCP.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
MCP client
Glama
MCP server
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
100% free. Your data is private.
Tool Definition Quality
A4.7/5.0
Tool DescriptionsA
Average 4.9/5 across 4 of 4 tools scored.
Server CoherenceA
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.
Available Tools
4 toolsfeature_requestAInspect
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.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| description | Yes | A short description of the feature you need. Examples: 'LaTeX output for equations', 'support for ODE constraints', 'GPU-accelerated search', 'larger dataset limits'. Helps prioritize development. |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| ok | Yes | |
| message | Yes | |
| description | Yes |
Tool Definition Quality
A4.7/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description provides significant behavioral context beyond the sparse annotations. It discloses that requests are logged and used for prioritization, implying a write side effect. It also details rate limits (5/hour per IP, 50/hour global) and the 500-character truncation rule, which are concrete constraints not derivable from annotations. This goes well beyond what structured fields offer.
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 tight and front-loaded, leading with the purpose and then providing essential constraints. Every sentence adds value (usage condition, logging, rate limits, truncation). It uses clear line breaks for readability without fluff.
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?
For a simple one-parameter tool with an output schema, the description fully covers use case, side effects, limitations, and rate limits. The parameters are well-documented, and the behavior is transparent. No critical information is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description covers 100% of the single parameter, including examples and purpose. The tool description adds the truncation limit, which is also parameter-relevant. Since the schema already does the heavy lifting, the extra note earns a score above baseline.
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 clearly states the tool's function with a specific verb and resource: "Request a feature that Occam doesn't support yet." It distinguishes itself from the sibling compute tools (pysr_run, etc.) by being focused on feature requests rather than scientific computation. The action is unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use the tool: "Use this when you need a capability that Occam doesn't currently offer." While it doesn't name alternative tools or exclusions, the context is clear given the unrelated siblings. It also adds practical constraints like rate limits and truncation behavior, which guide usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
pysr_runARead-onlyInspect
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
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| X | Yes | 2D 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. | |
| y | Yes | Target values, one per row of X. | |
| payment | No | Payment 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>"}. | |
| populations | No | Number of evolutionary populations for the search. Default 15, max 20. | |
| feature_names | No | Names for each variable/feature column. Defaults to x0, x1, ... | |
| loss_threshold | No | Optional 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_complexity | No | Maximum expression tree size. Higher allows more complex expressions. Default 20, max 25. | |
| stall_detection | No | When 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_seconds | No | Wall clock time limit in seconds. Free tier: max 60. Paid tier: max 300 (5 minutes). Default 60. | |
| unary_operators | No | Allowed 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_operators | No | Allowed binary operators, drawn from the fixed supported set: +, -, *, /, ^. Custom operators are NOT supported. Default: +, -, *, /. Pass [] for none. |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| warning | No | |
| best_loss | No | |
| stop_reason | No | |
| pareto_front | No | |
| queue_seconds | No | |
| best_complexity | No | |
| best_expression | No | |
| elapsed_seconds | No | |
| best_expression_latex | No |
Tool Definition Quality
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.
pysr_uncertaintyARead-onlyInspect
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;
uncertainty about *which* expression is correct is not quantified.
Bootstrap semantics:
- If y_sigma is supplied, uses parametric bootstrap
(y_b = y + Normal(0, y_sigma)). CI reflects user-stated
measurement noise.
- Otherwise uses residual bootstrap: fit once, resample residuals.
CI reflects estimated-from-residuals noise.
Only Float constants in the expression become free parameters.
Integers stay structural (the 2 in x**2 is a function-class choice,
not a fit constant). Expressions with no Float constants
(e.g. "x + y") will be rejected with a validation error.
Expression grammar: the `expression` string is parsed by sympy.
Accepted operators are the same set pysr_run emits: unary `sin`,
`cos`, `tan`, `exp`, `log`, `log2`, `log10`, `sqrt`, `abs`, `sinh`,
`cosh`, `tanh`; binary `+`, `-`, `*`, `/`, `^` (or `**`). Whitespace
and parenthesization are free. Every free symbol in the expression
must correspond to an entry in `feature_names` — an unrecognised
symbol is silently treated as a fresh sympy Symbol and the fit will
fail downstream rather than reject early. Parse failures (syntax
errors, malformed operators) surface as tool errors.
If `feature_names` is supplied, its length must equal the number of
columns in `X`; a mismatch is rejected with a validation error.
Pricing: always free, regardless of dataset size. This tool has no
`payment` parameter and is never subject to the x402/Stripe gate.
Large bootstrap jobs still count against the shared rate limit
below, so budget `n_resamples` accordingly.
Rate limit: 10 requests/hour per IP, 200/hour global, max queue
depth 20 (shared with sindy_run and pysr_run).
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| X | Yes | 2D 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. | |
| y | Yes | Target values, one per row of X. | |
| alpha | No | Significance level. 0.05 → 95%% CI. Default 0.05. | |
| x_grid | No | Optional 2D grid of feature values at which to report a prediction band. Must have the same number of columns as X. Omit to skip prediction-band computation. | |
| y_sigma | No | Optional per-point measurement standard deviations, or a single scalar applied to all points. When supplied, the helper uses parametric bootstrap (y_b = y + Normal(0, y_sigma)); otherwise it uses residual bootstrap. Supplying y_sigma also improves the initial weighted fit. | |
| expression | Yes | The expression to bootstrap, as returned by pysr_run (`best_expression` or a `pareto_front[i].expression`). Only numeric Float constants are treated as free parameters — integers in the expression (e.g. the 2 in x**2) stay structural. | |
| n_resamples | No | Number of bootstrap resamples. Higher = tighter CIs, more compute. Default 100. | |
| feature_names | No | Names for each variable/feature column. Defaults to x0, x1, ... |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| note | Yes | |
| alpha | Yes | |
| coefficients | Yes | |
| prediction_ci | No | |
| bootstrap_method | Yes | |
| n_requested_resamples | Yes | |
| n_successful_resamples | Yes |
Tool Definition Quality
A5/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations only declare readOnlyHint=true and destructiveHint=false, but the description adds extensive behavioral detail: parametric vs residual bootstrap semantics, treatment of Float vs Integer constants, sympy parsing behavior, silent handling of unrecognized symbols, validation rules, and rate limits. This far exceeds what annotations provide and helps the agent predict tool behavior accurately.
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 detailed but well-structured with clear sections (Typical flow, Bootstrap semantics, Expression grammar, Pricing, Rate limit). Every sentence provides necessary information without fluff. The front-loaded summary gives immediate clarity, and the logical progression makes it easy for an agent to extract key facts.
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 8 parameters, an output schema, and annotations, this description covers all necessary contextual aspects: purpose, usage flow, bootstrap methodology, parameter semantics, edge cases, pricing, and rate limits. It leaves no important gap for an agent deciding whether and how to invoke this tool.
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 critical meaning not present in the schema: y_sigma triggers parametric bootstrap, x_grid is for prediction bands, n_resamples trade-off, and the crucial rule that only Float constants are free parameters while integers stay structural. The expression grammar and parsing details are essential for correct invocation.
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 precise verb+resource statement: 'Bootstrap confidence intervals for the numeric constants of a frozen expression, plus optional prediction bands on an x-grid.' It clearly distinguishes from siblings by explaining the typical flow from pysr_run and noting that it does not perform posterior inference over expression structures.
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?
Provides explicit when-to-use context: 'Typical flow: call pysr_run, pick an expression from the response... pass it back here with the same dataset.' It also states exclusions (expressions with no Float constants are rejected) and clarifies limitations (uncertainty about which expression is correct is not quantified). This gives clear guidance on when this tool is appropriate versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
sindy_runARead-onlyIdempotentInspect
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_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 table as structured JSON: occam://pricing
Advisory limits: jobs over 500,000 rows or 50 variables are accepted
but may not converge within the time budget; the response carries a
top-level `warning` the agent should surface and treat as tentative.
If `feature_names` is supplied, its length must equal the number of
data columns; a mismatch is rejected with a validation error.
Rate limit: 10 requests/hour per IP, 200/hour global, max queue
depth 20 (shared with pysr_run and pysr_uncertainty).
Response (success) includes `equations[]` (each with `variable`,
`equation`, `expression`, `expression_latex`, `r2`), `library_terms`,
`nonzero_terms`, `elapsed_seconds`, `canonical_match` (dict with
`system`, `form`, `variable_map`, `parameter_map`, `confidence` if
the discovered system matches one of Lorenz / Lotka-Volterra /
Van der Pol / Duffing; `null` otherwise), optional `warning`,
optional `_meta` (MPP receipt on paid calls). Full response and
payment-required schemas: occam://tool-schemas
Example request:
data=[[1.0, 0.0], [0.95, -0.31], [0.81, -0.59]], t=[0.0, 0.1, 0.2],
feature_names=["x", "y"], poly_degree=2, threshold=0.1
Policy: occam://privacy-policy — Citation: occam://citation-info
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| t | Yes | Timestamps corresponding to each row of data. Length must match row count. | |
| data | Yes | 2D array of time series data. Each row is a timestep, each column is a state variable. Free tier: 100 rows, 8 variables. Paid tier: up to 500,000 rows, 50 variables. | |
| payment | No | Payment 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>"}. | |
| max_iter | No | Maximum STLSQ optimizer iterations. Default 20. | |
| threshold | No | STLSQ sparsity threshold. Higher values produce sparser equations. Default 0.1. | |
| poly_degree | No | Polynomial library degree for SINDy candidate functions. Default 2. | |
| feature_names | No | Names for each variable/feature column. Defaults to x0, x1, ... |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| warning | No | |
| equations | No | |
| library_terms | No | |
| nonzero_terms | No | |
| canonical_match | No | |
| elapsed_seconds | No |
Tool Definition Quality
A4.8/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnlyHint, idempotentHint), the description discloses significant behavioral traits: convergence warnings for large jobs, feature_names validation, pricing responses, canonical_match behavior, and rate limits. This adds rich context beyond what annotations provide, with no contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is long but well-structured, starting with the core purpose and then providing necessary operational details. Every sentence contributes useful information, though the pricing and rate limit details could potentially be condensed or linked out, making it slightly verbose.
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 (payment, rate limits, response schemas), the description is remarkably complete. It covers purpose, differentiation, pricing, limitations, validation, examples, and points to detailed schemas and policies, leaving little ambiguous for the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already has 100% coverage for parameters, so the baseline is 3. The description adds extra value by explaining the feature_names length validation, an example request, and free-tier row/variable limits that directly relate to parameter usage, elevating it above baseline.
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 clearly states the tool's function: 'Recovers governing differential equations (dx/dt = f(x)) from time series data.' It also distinguishes from siblings by explicitly directing users to pysr_run for algebraic relationships without time structure, which is a specific verb+resource statement.
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?
It explicitly provides when-to-use context: 'For algebraic y = f(x) relationships without time structure, use pysr_run instead.' Additionally, it gives clear guidance on free tier limits, payment requirements, and rate limits, which are crucial for correct usage.
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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