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FDA Data MCP

by io.github.medley

Search Drug Applications

fda_search_drugs
Read-onlyIdempotent

Search Drugs@FDA applications across all companies. Filter by sponsor name (fuzzy match), application number, brand name, or submission status. Returns application details including products (brand names, dosage forms, active ingredients) and submissions (approval dates, status). Related: fda_search_ndc (NDC-level product details including labeler and packaging), fda_drug_labels (structured product labeling/package inserts), fda_clinical_result_letters (Complete Response Letters — FDA refusal-to-approve decisions), fda_drug_shortages (active drug shortage data).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results to return (1-500)
offsetNoResult offset for pagination
statusNoSubmission status (searches submissions JSONB)
companyNoCompany name (fuzzy search)
brand_nameNoBrand name (searches products JSONB)
application_numberNoApplication number

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds useful behavioral context by noting fuzzy matching for company names and specifying that the tool returns application details including products and submissions. No contradictions or missing critical caveats.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences: the first states the core function and filters, the second lists return contents and related tools. It is well-structured, front-loaded, and contains no filler or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Even without an output schema, the description clearly states what will be returned (products, brand names, dosage forms, active ingredients, submissions, approval dates, status). It also names several sibling tools to help an agent route to a more specific tool when needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and every parameter has a description, so the schema already carries the main load. The description restates the filterable fields and adds the 'fuzzy match' detail for company, but it doesn't add significant meaning beyond the schema's parameter descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Search') with a clear resource ('Drugs@FDA applications') and scope ('across all companies'). It also distinguishes the tool from related siblings by mentioning the result fields and listing alternative tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides a 'Related' list with explicit alternative tools for NDC-level data, labels, clinical letters, and shortages, which helps an agent choose between tools. However, it doesn't state explicit when-to-use/when-not-to-use conditions beyond those alternatives.

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

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TDQS

A4.1/5.0
Disambiguation4/5

Most tools have distinct purposes with clear boundaries, such as fda_search_drugs for drug applications and fda_search_510k for device clearances. However, some overlap exists, like fda_device_udi and fda_device_udi_lookup both querying UDI data, which could cause confusion despite differences in scope.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern with a clear fda_ prefix, using descriptive verbs like search, get, list, and link. This uniformity makes the set predictable and easy to navigate, with no deviations in naming style.

Tool Count2/5

With 48 tools, the count is excessive for a single server, making it overwhelming and difficult for agents to manage. While the domain is broad (FDA data), the toolset feels bloated with many specialized or overlapping tools that could be consolidated.

Completeness5/5

The toolset provides comprehensive coverage of FDA data domains, including drugs, devices, inspections, compliance, recalls, and facilities. It supports full CRUD-like operations (e.g., search, get, link, save) and lifecycle workflows, with no obvious gaps for the intended purpose.

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