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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds real value with ranking behavior (best first), a similarity interpretation heuristic ('about 0.7 and up answers the question'), and a failure-recovery protocol. However, much of the return-shape detail (title, heading, updated_at, total_matches, has_more) overlaps with the existing output schema, so it goes beyond annotations but not dramatically.

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?

Front-loaded with purpose, then usage, then return shape, then a fallback ladder — a sensible order with no filler sentences. The parenthetical topic list is long but earns its place by disambiguating scope. A minor trim of the return-field enumeration would improve it, since the output schema already lists those fields.

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 9-parameter search tool with full schema coverage and an output schema, the definition supplies everything an agent needs: when to call it, how to read scores, how to interpret empty results, and how to escalate to broader modes or admit a gap. Nothing material is left unstated.

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 baseline is 3, and the description still adds meaning on top: it explains that mode 'broad' loosens matches and pulls neighbouring text, and that offset is for paging on after a weak result set. The similarity/score semantics also make min_score interpretable. Tags, category and document filters are left entirely to the schema, which is acceptable given full coverage.

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 (search) and resource (FastCAC's knowledge base) and then enumerates the content classes it covers — articles, policies, guides, how-tos — with concrete topic examples (pricing, refunds, CAC rules, post-registration steps). This makes it clearly distinct from sibling tools like search_cac_register or get_cac_documents without needing the schema.

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?

Gives an explicit trigger ('Use it before answering a question about FastCAC's service or CAC process that the instructions above don't settle') plus a directive to answer from passages rather than memory. It also supplies a recovery path for weak results (mode 'broad', reword, page with offset) and an honest-failure instruction. It does not name sibling alternatives or state when NOT to use it, which keeps it short of a 5.

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