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

The annotations already mark it read-only and non-destructive, and the description adds substantial behavioral context beyond those hints: matching accepts plain words and does not require exact referential wording, multi-skill phrases are split, commonSkills only contains what the person thought to cite, and the coverage rate must not be turned into a percentage or employability verdict. This is exactly the kind of non-obvious behavior an agent needs to know.

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 front-loaded with the core purpose and output, but it is long and repeats nearly all content in French and then English. While the warnings are valuable, the bilingual duplication makes it less concise than it could be. It is structured coherently, but length reduces clarity for an agent parsing the definition.

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?

There is no output schema, so the description must explain return semantics, and it does: matched skills, missing skills, separated know-how and knowledge, raw numbers, commonSkills, jobSkillsCount, and the coverage rate caveat. It also covers the two main misuse risks — computing a percentage and drawing employability conclusions — making it complete for this tool's complexity.

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?

The input schema already provides 100% parameter coverage, so the baseline is 3. The description adds meaning beyond the schema by explaining that userSkills should be in the person's own words, may contain multiple skills in one phrase, and that targetJobSlug is a required explicit target rather than an auto-suggested job. This enriches both parameters usefully.

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 states a specific verb and resource: it compares a list of user-provided skills against a target job's required skills and reports matches, missing skills, and raw counts. It also distinguishes itself from siblings by explicitly saying it does not suggest jobs, so an agent can tell it apart from get_similar_jobs or search_jobs.

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?

It gives a clear when-to-use context: answering 'do I have the profile for job X'. It also gives an explicit exclusion: it does not suggest jobs, so a target must be supplied. However, it does not name sibling alternatives directly, so the guidance is strong but not a full alternative-routing instruction.

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