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

No annotations are present, so the description carries the full transparency burden and does it well: it discloses API-key gating, the subset of results anonymous callers see, risk-class non-portability across markets, the observed-practice caveat, and count-based name resolution. These are behavioral traits a caller could not infer from the schema alone.

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?

The description is long but dense, with every sentence carrying distinct value and the most operationally urgent fact about API-key access front-loaded. It could be broken into clearer sections for scannability, but there is no filler or repetition.

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 tool with no output schema and no annotations, the description covers the main returned elements (resolution, product codes, risk table, sample sizes), the key caveats, and the accepted input forms. The notable gap is that all parameters are optional in the schema while the description never explicitly states that exactly one of the three inputs should be supplied.

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?

The input schema already documents all three parameters, but the description adds substantial meaning: naming conventions, US/UK spelling resolution, no-brand/no-use guidance, how ambiguous names are resolved and reported, and the suggested follow-up call with gmdn_code. This goes well beyond the schema's example values.

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 names a concrete verb and resource: it gives a classification view of a device type, enumerating the GMDN hierarchy, FDA product-code mappings with counts, and per-market risk classes with sample sizes. This is specific enough to distinguish the tool from all sibling tools without requiring additional schema inspection.

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?

It tells the caller exactly what inputs are acceptable (a plain device name, GMDN code, or FDA product code) and how to phrase device_type, including avoiding brand names or use descriptions. It also explains how to follow up on ambiguous resolution via gmdn_code, but it does not explicitly name sibling tools or state when not to use this tool.

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