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

Annotations already provide readOnlyHint and idempotentHint, and the description adds value by specifying the return content (reports with outcomes, reactions, product details) and the date format expectation. No contradictions with annotations; it supplements them with useful behavioral context.

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?

A single, efficient sentence that leads with the action verb and resource, then lists filters and expected output. Every phrase carries meaning with no redundancy or fluff.

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?

Given the tool has no output schema, the description appropriately mentions the report contents. It covers the main query dimensions and return details, but could optionally mention pagination defaults or that the date range applies to date_created. Still, it is sufficiently complete for a straightforward search tool with well-annotated schema and safety hints.

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 each parameter well-described in the input schema. The description only summarizes the filter categories (product area, reaction, date range) without adding new semantics beyond what the schema already provides. This aligns with the baseline of 3 for high schema 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?

The description clearly states the tool searches consumer adverse events specifically for food and cosmetic products, with explicit filter dimensions (product area, reaction keyword, date range) and output content (outcomes, reactions, product details). This distinguishes it from sibling tools like fda_vet_events or generic enforcement searches.

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?

The description implies the usage context: use when searching consumer adverse events for food/cosmetic products with optional filters. It does not explicitly mention alternatives or exclusions (e.g., not for veterinary events), but the context is clear enough for an agent to infer when to select this tool over others.

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