FDA Data MCP

Search Facilities

fda_search_facilities

Read-onlyIdempotent

Search FDA-registered facilities by name, city, state, or country. Searches drug (DECRS) and device registration databases. Returns FEI number, name, address, and source. Use the operations parameter to filter by manufacturing type (e.g., 'Contract Manufacture', 'API', 'Repack'). Use country filter (ISO code: US, DE, IN, CN, IE) to map a company's global manufacturing footprint. Excludes: products at facility, inspection history, enforcement actions. Related: fda_get_facility (full facility detail by FEI including products and operations type), fda_inspections (inspection data by FEI), fda_citations (CFR violations by FEI).

Input Schema

TableJSON Schema

Name	Required	Description
`city`	No	City name
`limit`	No	Max results to return (1-500)
`state`	No	State code (e.g. CA, NY)
`offset`	No	Result offset for pagination
`company`	No	Facility or company name (fuzzy search)
`country`	No	ISO country code (e.g. US, DE)
`operations`	No	DECRS operations keyword

TDQS

A4.7/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds behavioral context by stating it searches two specific databases, returns FEI number/name/address/source, and explicitly excludes related data categories. No contradiction with annotations; the added scope goes beyond what annotations provide.

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 four sentences, each adding a distinct piece of information: purpose, operations parameter guidance, country filter usage, and exclusions/related tools. No redundant phrasing or filler; every sentence earns its place.

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?

Despite having 7 parameters and no output schema, the description covers search dimensions, return fields, exclusions, and related tools. The schema fully documents each parameter, and annotations confirm read-only safe operation, leaving no significant context gaps.

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

Parameters4/5

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

Schema description coverage is 100%, so baseline is 3. The description adds value by giving concrete examples for operations ('Contract Manufacture', 'API', 'Repack') and country (ISO codes US, DE, IN, CN, IE), and explains the strategic purpose of the country filter for mapping global manufacturing footprint.

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 opens with 'Search FDA-registered facilities by name, city, state, or country,' which clearly states the verb, resource, and scope. It further distinguishes itself by specifying that it searches drug (DECRS) and device registration databases and mentioning related tools like fda_get_facility, fda_inspections, and fda_citations.

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

Usage Guidelines5/5

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

It explicitly instructs when to use the operations parameter ('filter by manufacturing type') and country filter ('map a company's global manufacturing footprint'). It also names alternatives and lists exclusions ('products at facility, inspection history, enforcement actions'), giving clear decision support.

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.

Resources

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