device_evidence_lookup

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

No annotations are provided, so the description fully bears the burden. It discloses that the tool returns parsed summary fields with source quotes and page numbers, and clarifies that null means the information was not stated. This is sufficient for a safe, read-only operation.

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 extremely concise with two sentences. The first sentence states the purpose and input, the second explains the output and null behavior. Every sentence adds unique information without redundancy.

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 simple one-parameter input and no output schema, the description adequately covers the output structure (list of fields) and special cases (null). Minor gaps include lack of error handling or authentication details, but these are not critical for this lookup tool.

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 schema defines the k_number parameter with pattern and description. The description adds value by providing an example (K252148) and specifying the domain (AI/ML-enabled devices), which clarifies the intended use beyond the schema.

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 looks up structured premarket evidence for AI/ML-enabled devices using a 510(k) number. It lists the specific fields returned (validation study design, sample sizes, etc.) and distinguishes from sibling tools like device_risk_lookup and evidence_search by its focused scope.

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 usage when a specific device number is known and premarket evidence is needed. It does not explicitly exclude other scenarios or mention alternatives, but the context from sibling tool names provides adequate guidance.

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