gpt_informal_prover

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

No annotations are provided, so the description must carry the full burden. It mentions the return format (List[str] with solution and verification_result) but does not disclose limitations, potential for incorrect outputs, or randomness effects despite a temperature parameter. The claim 'GPT's math skills are outstanding; you can trust the answers he gives you' is an unsupported assertion rather than a factual behavioral disclosure.

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

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

The description is verbose and includes extraneous content: the trust claim about GPT, and a lengthy workflow involving Gemini and leandex that is tangential to the tool's own behavior. The key information is buried within a long paragraph, making it less scannable. A shorter, focused description would be more effective.

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

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

The tool is part of a complex pipeline, but the description leaves critical ambiguity: it tells the user to ask Gemini for a solution while this is a GPT tool, and the 'switch to informal_prover' line is unclear. It does document the return value, but the overall workflow instructions are confusing and incomplete, making the tool difficult to invoke correctly in context.

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

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

Despite 0% schema description coverage, the description explains all three parameters: math_problem as 'the detailed text description', model as optional with default 'gpt-5.2-pro', and temperature as controlling randomness with default 0.7. This adds meaningful semantics beyond the bare schema, though it could further clarify model choices or temperature range.

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

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

The description clearly states the tool's purpose: 'Use OpenAI GPT model to solve math problems and provide detailed solution' and 'takes a raw math problem string, solves it, and explains the reasoning step-by-step.' This distinguishes it from sibling tools like gemini_informal_prover by explicitly naming the GPT model and the informal proving task.

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

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

The description offers some explicit guidance: 'Use this tool frequently for natural language math problems.' It also outlines a workflow involving leandex and switching to informal_prover when mathlib infrastructure is missing. However, the instruction to 'ask Gemini for a detailed solution' is confusing for a tool named gpt_informal_prover, and the reference to switching to informal_prover is ambiguous, undermining clear when-to-use guidance.

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