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

Annotations already declare readOnly/idempotent/openWorld, and the description adds substantive context beyond them: the matching algorithm (stopwords like Inc/LLC ignored, all remaining words must appear in the firm name), the exact fields returned, the 10-most-recent cap, and explicit false-positive/false-negative warnings. It also discloses the 2012 coverage boundary and the FDA vs USDA jurisdictional limit.

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 action and data source, followed by the matching rule, then return shape, then caveats, then scope. Every sentence carries distinct information; nothing is redundant with annotations or schema.

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 no output schema, the description fully specifies the return payload (total count plus the 10 most recent reports with firm, location, product, reason, classification, status, dates, recall number). It also covers the matching semantics, data vintage, and domain boundaries an agent needs to call and interpret this 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%, which sets a baseline of 3, but the description goes further by explaining how each name string is matched (ignored corporate suffixes, conjunctive word matching). The schema already caps the array at 10 names, so the description's added matching semantics are the real value here.

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 (find), resource (FDA food recall/enforcement reports), matching key (recalling firm name), and data source (openFDA food/enforcement since 2012). This is unambiguous and cannot be confused with the unrelated sibling oregon_license_check.

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 clear conditions for use: supplying supplier/manufacturer names, with explicit caveats that a name match may be a different firm and that no match does not imply safety, plus scope limits (FDA food only, not USDA meat/poultry). It does not name any alternative tool or when not to call it, so it falls 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.