fda_device_enforcement

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

The description adds behavioral context beyond the readOnly annotation, such as the note that records before June 2012 may lack fields and the inclusion of count fields (voluntary_mandated.exact, classification.exact, status.exact). These details are useful for setting expectations about data availability and aggregation. The readOnlyHint is consistent with the description, so no contradiction.

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 well-structured with a clear purpose statement, a useful note, and a list of example searches. It is somewhat longer than necessary but every section adds practical value. The front-loading of the main purpose is effective.

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 search tool with only two parameters and rich annotations, the description covers key aspects: purpose, classification system, data caveats, example queries, and count fields. It does not describe the return structure, but no output schema exists, and the provided information is sufficient for basic usage. The lack of explicit alternative tool comparison slightly reduces completeness.

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?

Although the schema already describes both parameters (100% coverage), the description enhances the 'search' parameter meaning by providing specific field examples (classification, report_date, recalling_firm) and count fields. This goes beyond the generic schema description and gives actionable query patterns.

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 verb and resource: 'Search FDA device recall enforcement reports.' This is specific and informative. However, it does not explicitly distinguish itself from the sibling tool fda_device_recalls, relying on the term 'enforcement reports' to suggest a distinct dataset.

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?

There is no guidance on when to use this tool versus alternatives like fda_device_recalls or fda_drug_recalls. The provided examples demonstrate search syntax but do not offer usage context or exclusions. This leaves the agent without clear decision criteria for selecting this tool.

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