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

The annotations already declare readOnlyHint=true, idempotentHint=true, and openWorldHint=false, so the safety profile is covered. The description adds useful behavioral context: searching multiple datasets simultaneously and returning matches with product codes and company names. This goes beyond the annotations without contradicting them.

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 three sentences with no filler. The first sentence front-loads the core action and scope, the second specifies datasets, and the third gives usage context and return contents. Every sentence contributes value.

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 no output schema, the description adequately explains return values (matches from each dataset with product codes and company names) and covers the search scope. It could mention pagination implications for large result sets, but the schema already documents limit/offset, so this is not a critical gap.

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%, giving baseline 3. However, the description expands on the product_name parameter by listing accepted name types (device name, trade name, generic name, brand name), which adds meaning beyond the schema's 'Product or brand name (fuzzy search)'. This additional detail justifies a 4.

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 a specific verb ('Search') and resource ('FDA device and drug datasets') and enumerates the exact datasets covered (device classifications, 510(k) clearances, PMA approvals, NDC records). This distinguishes it from sibling tools like fda_search_510k and fda_search_ndc, which target single datasets.

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 explicitly says 'Use when you know a product name but not which dataset it's in,' providing a clear when-to-use condition. However, it does not name alternative tools or state when not to use it, so it falls short of the full 'when/when-not/alternatives' ideal.

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