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

Annotations already declare readOnlyHint, idempotentHint and openWorldHint=false, so the safety profile is covered. The description adds genuinely useful behavior beyond that: results are ranked by word overlap with the title, and an empty list means no guide matched, plus it states the return payload is titles and URLs to cite. It stops short of latency/rate-limit detail, but with full annotation coverage that is a minor omission.

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 the core verb, corpus size and domain, then the return format, then the disambiguation, then the ranking semantics. The parenthetical topic list is the only dense passage, and it earns its place by telling the retriever what kinds of questions will match.

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?

No output schema exists, so the description correctly compensates by stating what comes back (titles and URLs to cite). Both parameters are fully covered by the schema, required/optional status is clear, and the corpus scope is bounded (kabo.co.in only), giving an agent everything needed to call it correctly.

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% with per-parameter descriptions and numeric bounds on limit, so the schema does the heavy lifting. The description still adds value by revealing that 'query' is interpreted as keywords matched against titles rather than semantic search, which shapes how an agent should phrase the query.

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 (keyword search) over a specific, quantified resource (1,308 Indian nutrition guides on kabo.co.in), lists the topical coverage, and states the return shape (titles and URLs to cite). It is unmistakably distinct from lookup_food and the product-comparison siblings.

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 a concrete triggering example ('vitamin D deficiency in vegetarians') and an explicit exclusion routing to lookup_food for a single food's values. The only gap is that it does not distinguish itself from compare_foods or protein_target, though those cover clearly different intents.

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