pass_to_ai_for_comments

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

With no annotations provided, the description must carry the transparency burden. It does disclose that the tool can add comments to existing files or create new files, which are key behavioral traits. However, it omits details about whether existing comments are overwritten or how file modifications are handled, leaving some ambiguity.

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?

Two concise sentences effectively convey the tool's purpose, workflow step, and capabilities. Every sentence contributes meaningful information without redundancy.

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 well-suited to its workflow context, with sibling tools and step numbering providing additional context. Parameter details are fully covered in the schema. However, it lacks information about output format or prerequisites, which would enhance completeness.

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%, with each parameter already described in the schema. The tool description adds no extra parameter semantics beyond what the schema provides, so the baseline 3 is appropriate.

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: having AI write comments with @spec-impl markers based on a design document. It also specifies it's the 3rd workflow step, distinguishing it from sibling tools like requirements, design, and implementation.

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 explicitly positions the tool as the 3rd step in a workflow, providing clear sequencing context. It doesn't explicitly state when not to use it, but the sibling tool names and step numbering strongly imply appropriate usage scenarios.

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