chatgpt_consult

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states that the tool consults models, but does not mention potential costs, latency, non-determinism, external API calls, or that responses may vary. It also does not clarify whether the tool is read-only or if it has side effects. This is a significant gap for a tool that presumably makes external calls.

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 a single, well-structured sentence that front-loads the purpose and lists concrete use cases. It is concise and free of fluff. However, it could have been slightly more informative without losing conciseness, such as mentioning the difference from 'chatgpt_review' or providing a brief example, so it earns a 4 rather than a 5.

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?

Given the tool has 6 parameters (1 required), no output schema, and no annotations, the description is insufficient. It does not clarify the expected input format for 'prompt' or 'context', does not explain how 'temperature' interacts with 'reasoning_effort', and does not differentiate this tool from the sibling 'chatgpt_review'. An agent lacks enough context to call it correctly or know what to expect.

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?

Schema description coverage is 100%, so all six parameters (model, prompt, context, temperature, system_prompt, reasoning_effort) are already documented in the schema. The description does not add any parameter-specific meaning beyond the schema, but the high coverage justifies a baseline score of 3. No additional context or examples are provided to enhance parameter understanding.

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 function: consulting ChatGPT/OpenAI models for high-level technical advice, architectural review, edge case analysis, or second opinions. The verb 'consults' and resource 'ChatGPT/OpenAI models' are specific, and the use cases are enumerated. However, it does not explicitly distinguish from the sibling tool 'chatgpt_review', which likely overlaps in purpose, so it loses a point for lacking sibling differentiation.

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 implies appropriate usage by listing contexts like 'high-level technical advice, architectural review, edge case analysis, or second opinions'. However, it does not explicitly state when to use this tool instead of alternatives like 'chatgpt_review', nor does it mention any exclusions or prerequisites. The guidance is present but implicit, relying on the agent to infer applicability.

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