Occam

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.

Input Schema

TableJSON 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

TableJSON Schema

Name	Required	Description	Default
`ok`	Yes
`message`	Yes
`description`	Yes

TDQS

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.

All Occam tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources

Need Help?