FDA Data MCP

Company Lookup

fda_lookup_company

Read-onlyIdempotent

Quick company lookup: facilities (with addresses and operations) and enforcement actions (recalls) for a single company and its known aliases. Costs 1 credit. Excludes: 510(k) clearances, PMA approvals, drug applications, inspection history, and subsidiary data. Related: fda_company_full (adds clearances/approvals/drugs for 5 credits), fda_suggest_subsidiaries (discover related entities), fda_get_facility (per-facility products and operations by FEI).

Input Schema

TableJSON Schema

Name	Required	Description
`company`	Yes	Company name to look up
`facilities_limit`	No	Facilities result limit
`enforcement_limit`	No	Enforcement result limit
`facilities_offset`	No	Facilities result offset
`enforcement_offset`	No	Enforcement result offset

TDQS

A4.7/5.0

Behavior5/5

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

Annotations declare readOnlyHint and idempotentHint, and the description adds meaningful context beyond annotations: cost per call, alias handling, inclusion of facilities with addresses/operations, enforcement recalls, and explicit exclusions. This gives a clear behavioral contract without contradicting annotations.

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?

Three sentences: purpose, exclusions, and related tools. Every sentence adds value, no fluff, and the most important information (what it does, cost, exclusions) is front-loaded.

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 no output schema, the description covers return content (facilities with addresses/operations, enforcement recalls), cost, alias behavior, and exclusions. It also points to related tools for broader data, making the tool's place in the ecosystem clear and sufficient for use.

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% with descriptions for all five parameters, so the baseline is 3. The description does not add any parameter-level detail beyond what the schema already provides; it mentions company lookup but does not elaborate on limits or offsets.

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 clearly states the tool performs a 'quick company lookup' for facilities and enforcement actions, with specific attributes (addresses, operations, recalls). It distinguishes from siblings by listing exclusions (510(k), PMA, drugs, inspections) and naming related tools.

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?

Explicitly says 'Costs 1 credit' and 'Excludes' important categories, guiding when to use this tool versus alternatives. It names related tools with their differing scope and cost, such as fda_company_full for 5 credits and fda_suggest_subsidiaries for discovering related entities.

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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