fda_approved_drugs

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

Annotations already cover read-only, open-world, idempotent, and non-destructive behavior, so the description doesn't need to restate those. It adds valuable context by specifying the output fields (approval history, active ingredients, marketing status) and provides example query syntax using OpenFDA query operators. This goes beyond the annotations and helps the agent understand what to expect from the tool. No contradiction with annotations.

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 concise and well-structured: it opens with the purpose, then lists what it shows, followed by three concrete example queries. Every sentence adds value, and the examples are front-loaded to illustrate usage quickly. There is no redundant text or unnecessary detail.

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 no output schema, the description provides sufficient information about what the tool returns (approval history, active ingredients, marketing status). It also demonstrates the query syntax through examples, covering the main usage pattern. It does not mention pagination or error handling, but those are minor given the tool's simplicity and the presence of the limit parameter in the schema.

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 descriptions cover both parameters fully (100% coverage), so the baseline is 3. The description adds domain-specific examples (e.g., 'openfda.brand_name:"Ozempic"') that are directly applicable to this tool, making the search parameter more meaningful. These examples show the exact field names and value formats, which is valuable beyond the generic schema description.

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's purpose: 'Search FDA-approved drugs (Drugs@FDA database).' It specifies the verb 'Search' with a resource and lists the exact search dimensions (brand name, sponsor, application number). This distinguishes it from sibling tools like fda_drug_events (adverse events) and fda_drug_labels (label text) by focusing on the approved-drug database and its unique output fields.

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 provides clear context: it is for searching approved drugs and shows what it returns. It does not explicitly mention alternatives or when not to use it, but the purpose is specific enough that an agent can infer it should be used for approval-related queries. Example searches demonstrate the query format, which adds practical guidance. However, it lacks explicit exclusions such as 'do not use for adverse events or labels.'

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