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

No annotations are provided, so the description carries the behavioral disclosure burden. It reveals that feedback is recorded publicly on GitHub and contributes to a trust score, which is critical context for users who might otherwise assume private submission. It does not cover aspects like edit/delete limitations, but the provided details are meaningful.

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 compact yet rich, using two sentences with embedded real-world examples and an explicit invocation guideline. Every phrase earns its place, and the structure front-loads the core purpose before adding contextual details.

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 simple, well-annotated tool, the description covers the necessary context: what the tool does, the public nature of the feedback, and when to use it. With no output schema, the description need not explain return values, and nothing essential is missing for an agent to invoke this correctly.

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 coverage is 100%, so the schema documents all three parameters. The description adds value by giving concrete examples for the 'notes' field and by clarifying that both 'worked' and 'problem' results are helpful, reinforcing the enum meaning beyond the schema.

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 action as reporting real-world usage feedback for a part, with concrete examples of what constitutes feedback. It distinguishes this from sibling tools like get_part, search_parts, and request_part by focusing on contribution of usage experience.

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 states when to use the tool ('after actually using a part') and encourages reporting both successes and problems. It does not explicitly name alternatives or when-not-to-use cases, but the context is clear enough to prevent misuse.

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