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

Annotations mark this as non-readOnly and non-idempotent, which is ambiguous, but the description clarifies the output is findings 'with fixes' and discloses the PRO entitlement plus AI-consent prerequisite — context annotations do not carry. What remains unstated is whether any CV content is modified; 'with fixes' implies suggestions rather than writes, but this is not made explicit.

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 sentences plus two short clauses; the core purpose is front-loaded and every fragment carries information. The 'PRO and AI consent.' sentence is telegraphic but efficient rather than padded.

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 two-parameter analysis tool with full schema coverage, the description covers purpose, audience, prereqs, and that fixes are returned. With no output schema present, it could say more about the shape of the findings, which is the only meaningful gap.

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%, and the cvId field even points to list_my_takemeup_cvs. The description adds nothing about cvId or the locale enum, so baseline 3 applies since the schema does all the work.

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?

Names a specific verb ('Find what makes... look dated') and resource (the user's CV), and enumerates concrete signals: old technologies, outdated phrasing, graduation years revealing age. It is clearly distinct from siblings like roast_takemeup_cv or run_takemeup_authenticity, though it does not explicitly name or contrast with any of them.

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?

'Use for experienced candidates worried about age bias' gives a clear usage condition and audience. It also flags prerequisites ('PRO and AI consent'), but offers no explicit exclusions or alternative tools for adjacent analyses such as buzzword detection.

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