chatgpt_ask

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

With no annotations, the description carries the behavioral transparency burden. It meaningfully discloses retry/idempotency semantics ('same request_id and identical inputs') and that file paths are used for local context collection. It stops short of explaining output shape, failure modes, or cancellation, but the provided behavior is genuinely useful.

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?

Three sentences with no filler; each sentence contributes a distinct piece of information: purpose, context collection, and retry behavior. The most important decision-relevant content is front-loaded.

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?

For a five-parameter tool with no output schema and no annotations, the description covers high-level use cases, local context collection, and retry behavior. However, it omits what the response looks like, how repo_path and conversation_handle affect execution, and how this relates to chatgpt_cancel. It is workable but has notable gaps.

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 0%, so the description must compensate for the five parameters. It partially explains file paths/context collection and request_id's role in retries, but it leaves prompt, repo_path, and conversation_handle semantically unexplained. The compensation is insufficient for the parameter count.

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 opens with a specific action ('Delegate') and names the resource (ChatGPT), followed by concrete task categories: design comparisons, difficult debugging, and lengthy analysis. It is clear about the tool's purpose but does not explicitly contrast it with sibling tools like review_with_chatgpt or chatgpt_cancel.

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?

It provides concrete scenarios where the tool is appropriate and gives actionable guidance: 'Call once and wait; retry uncertain outcomes with the same request_id and identical inputs.' It does not state when not to use it or point to alternatives, so it lacks explicit exclusions but still gives clear usage context.

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