review_file

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 clearly states the tool builds a prompt and does not call the LLM directly, which is a critical behavioral trait. However, it does not disclose what the output format is (e.g., returned string, saved file), nor any side effects or prerequisites.

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 sentence that is compact and front-loaded with the key action and resource. No extraneous words or repetition. Every part of the 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?

The description is incomplete for a tool with 4 parameters and no output schema. It does not explain what the tool returns (e.g., the prompt as a string, a file path, or something else). The missing return value is a significant gap that hinders an agent from correctly using the tool's output.

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 each parameter already has a description. The overall description adds context that the parameters are used to construct a review prompt, which enhances understanding. However, it does not add specific meaning beyond what the schema provides, such as how 'style' influences the prompt or the role of 'commitMessage'.

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 builds an LLM prompt for single file review and scoring, and explicitly notes it does not directly call the LLM. This verb+resource pairing is specific. However, it does not explicitly differentiate from siblings like 'review_code' or 'review_diff', relying on the indirect implication that those are direct reviewers rather than prompt builders.

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 provides no explicit guidance on when to use this tool versus alternatives. It implies the tool is for building a prompt rather than performing a direct review, but does not state conditions, exclusions, or mention sibling tools. The agent is left to infer usage context without clear direction.

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