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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuinely new behavior: keyword normalization ('break-even', 'breakeven' and 'break even' are one term, plurals match), the shape of returned passages (id, kind, title, link, source), a data caveat that targets are RestaurantDoctorAI's own rather than industry benchmarks, and a privacy note that queries aren't stored.

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?

Purpose is front-loaded in the first clause and every subsequent sentence carries information (corpora list, matching rules, return shape, targets caveat, privacy). It is a dense single block with repetitive 'RestaurantDoctorAI's' phrasing that could be tightened, but nothing is padding.

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?

With an output schema present, the description need not document return structure in depth, and it still summarizes it. It covers what is searchable, how matching works, and the one semantic trap (targets are not benchmarks). The only meaningful gap is routing guidance relative to the sibling knowledge tools and specialized calculators.

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%, so all three parameters are already documented, including the enum values and the limit range. The description restates kind narrowing and adds only the keyword-matching semantics for query, which is useful but modest beyond what the schema provides; baseline 3 is appropriate.

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 ('Searches RestaurantDoctorAI's sourced library') and then enumerates the six content corpora covered, which is far more precise than the title. It does not name a sibling it is not, so an agent must infer from context that this is the free-text search counterpart to list_knowledge/read_knowledge and the dedicated calculators.

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?

Usage is implied through 'Keyword search' and the note that 'kind narrows the search to one kind of entry', which tells an agent this is for lookup rather than computation. However, there is no explicit when-to-use/when-not guidance and no mention of list_knowledge or read_knowledge as the alternatives for browsing or retrieving full entries.

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