review_with_chatgpt

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

No annotations or output schema are present, so the description carries the full transparency burden. It adds useful operational behavior: the tool 'collect[s] the diff and matching file contents directly from repo_path' and the agent should 'not paste code.' It also says the tool will 'return actionable findings.' Still, it does not disclose side effects, external call behavior, latency, failure modes, or output structure.

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 four short sentences with no filler. It front-loads the usage rule, then gives an operational note, a scoping tip, and the expected output type. Every sentence earns its place.

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 six-parameter tool with zero schema coverage and no output schema, the description is too thin. It covers the overall workflow and timing, but leaves important invocation details undocumented: what each scope value means, what base_ref is for, how question/request_id are used, and what 'actionable findings' look like structurally.

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 six parameters, but it only maps to two of them: 'repo_path' (collect directly from repo_path) and 'paths' (narrow paths). The key parameters 'scope', 'base_ref', 'question', and 'request_id' are left unexplained, including the meaning of the scope enum values.

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 identifies the tool as the primary option for 'substantial code reviews' and says it returns 'actionable findings', so an agent can tell it is a review-focused ChatGPT tool. It does not explicitly distinguish it from siblings like chatgpt_ask, but the review-specific language and the name make the purpose unambiguous.

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 gives explicit timing guidance: 'Use first for substantial code reviews and once after non-trivial implementation.' It also advises to 'narrow paths for focused reviews.' However, it never states when not to use it or what alternative tools to prefer, stopping short of full routing guidance.

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