Fetch the FDA-approved drug label for a brand or generic name. Includes indications, dosage, contraindications, warnings, adverse reactions.
| Name | Required | Description | Default |
|---|---|---|---|
| drug_name | Yes | Brand or generic, e.g. 'Ozempic' or 'semaglutide'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description does not disclose behavioral traits like error handling, rate limits, or authentication needs, leaving gaps for a fetch 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?
Two concise sentences, front-loaded with purpose and content list, no wasted words.
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 simple tool with one parameter and no output schema, the description covers the main content but does not explain output format or error cases, leaving minor gaps.
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% and the description for 'drug_name' in the schema already explains it; the description adds no additional meaning beyond the schema's parameter 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 'Fetch the FDA-approved drug label' specifying the action and resource, and distinguishes from sibling tools like fda_adverse_events by focusing on label content.
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?
No explicit guidance on when to use this tool vs alternatives or when not to use it; lacks context for choosing among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct FDA data domain: adverse events, drug approvals, labels, and recalls. No semantic overlap exists between them.
All tools follow a consistent 'fda_<domain>_<action>' pattern, using underscores and clear descriptive names. The two drug-related tools share the 'fda_drug_' prefix.
Four tools are well-suited for the FDA Approvals domain, covering the most common requests (approvals, labels, recalls, adverse events) without being overwhelming.
Core FDA drug information is covered, but clinical trial data or enforcement reports are missing. Agents can work around the minor gap.