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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), and the description adds non-obvious behavioral context: results are restricted to the previously analyzed corpus and are ranked by similarity plus score. It leaves the meaning of the score and the result shape unexplained, which keeps it from a 5.

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 compact sentences with the capability, mechanism, an example, and the constraint front-loaded before the alternative. Every clause carries information; no filler.

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, and the description does not describe what a result contains (person fields, score, evidence) or how the similarity/score ranking blends together. Combined with completely undocumented nested filter fields, the definition is usable but leaves real gaps for correct invocation.

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 75%, below the threshold where the schema alone suffices, and the undocumented part is the entire nested `filters` object (org, company, category, languages, min_stars) — the most semantically ambiguous parameter. The description says nothing about filters, does not explain the interaction between `threshold` and ranking, and only restates 'plain-language' for query, which the schema already says.

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?

States a specific verb and resource ('Plain-language search over developers AyHire has already analyzed'), plus the ranking mechanism (semantic similarity plus score ranking) and a concrete example query. This clearly separates it from siblings like search_people or find_people_for_role.

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?

Explicitly states the limitation 'Only finds people analyzed before' and routes the agent to analyze_github_user when the person is missing — a genuine when/when-not with an alternative. It does not, however, distinguish itself from the similarly named search_people, which an agent could easily confuse with this tool.

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