Get drug label, safety info, adverse events, or recall data from OpenFDA. Returns brand names, generic name, indications, warnings, dosage, interactions.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | Drug name (e.g. metformin, aspirin, lisinopril) | metformin |
| type | No | label (default), adverse_events, or recalls | label |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It does not disclose whether this is a read-only operation, external API dependencies, rate limits, or any limitations. It only states what data it returns, which is more output than behavioral context.
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 with no redundancy. The main action is front-loaded, followed by the return fields. Every word earns its place.
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 no output schema, the description is reasonably complete, but it does not clarify how return fields differ by 'type' (e.g., recalls vs. adverse events). This could mislead users expecting the same fields for all types.
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 already describes both parameters with 100% coverage, so the description's mention of types (label, adverse events, recalls) reinforces but does not add meaning beyond what is in the schema. It does not clarify parameter relationships or formatting.
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 specifies the exact resource (drug label, safety info, adverse events, recall data) and source (OpenFDA), clearly distinguishing this from sibling tools like get_compound_data or get_drug_targets. It also lists return fields, making the scope unambiguous.
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 use for drug information but does not explicitly state when to choose this tool over siblings or mention exclusions. It relies on the reader to infer the use case, so there is no explicit alternative guidance.
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 queries a distinct public database (PubChem, OpenFDA, ChEMBL) and returns clearly non-overlapping data types: chemical properties, label/safety info, and target/bioactivity. Despite the shared drug/compound domain, the descriptions leave no ambiguity about which tool to use.
All tool names follow the exact pattern 'get_' + descriptive noun phrase (compound_data, drug_info, drug_targets), making the naming scheme highly predictable and consistent.
With only three tools, the server is minimal yet well-scoped for its purpose of retrieving drug/compound information from multiple sources. Each tool adds a distinct value and the count is within the ideal range for a focused server.
The server covers three major aspects of drug information: chemical identifiers, regulatory/safety data, and target/bioactivity. However, there is no search/discovery tool to locate drugs by query, and additional areas like clinical trials or pricing are missing. Still, the core lifecycle of drug data lookup is adequately covered.