FDA Data MCP

Recall-to-Facility Trace

fda_recall_facility_trace

Read-onlyIdempotent

Trace a recall to its candidate manufacturing facility with explicit confidence levels. Matches by firm name, NDC lookup, and facility registration data. Returns the recall details, matched facility candidates with FEI numbers and confidence scores, and match methodology. Provide either recall_number (from fda_search_enforcement or fda_ires_enforcement) OR firm (+ optional product) to resolve the best-matching recall automatically. Related: fda_get_facility (full detail for matched FEI), fda_inspections (inspection history for matched FEI), fda_compliance_actions (warning letters for matched FEI).

Input Schema

TableJSON Schema

Name	Required	Description
`firm`	No	Recalling firm name (fuzzy). Convenience path: resolves the best-matching recall when recall_number is omitted.
`product`	No	Optional product keyword to narrow firm-based resolution (matches product_description).
`recall_number`	No	Recall number (preferred). If omitted, provide firm to auto-resolve.

TDQS

A4.9/5.0

Behavior5/5

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

Even though annotations already declare readOnlyHint and idempotentHint, the description adds meaningful behavioral detail: the tool resolves the best-matching recall automatically on the firm path, uses fuzzy matching, and returns explicit confidence levels and match methodology. This goes beyond the structured annotations and helps the agent predict behavior accurately.

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 concise and front-loaded with the core purpose. It uses four sentences to convey purpose, matching methods, output contents, and usage alternatives without unnecessary detail. Every clause 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?

For a tool with 3 optional parameters and no output schema, the description fully covers what the agent needs: input options, resolution behavior, return contents, and related tools for further enrichment. It is self-sufficient for selection and invocation.

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 coverage is 100% and each parameter has a description, but the description adds value by explaining the relationship between parameters: recall_number is preferred, firm is a convenience path that auto-resolves, and product is optional to narrow firm-based resolution. This contextual parameter guidance exceeds the baseline schema-only semantics.

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 a specific verb and resource: 'Trace a recall to its candidate manufacturing facility with explicit confidence levels.' It clearly states the matching methods (firm name, NDC lookup, facility registration data) and outputs (recall details, facility candidates with FEI numbers, confidence scores, methodology), distinguishing it from sibling tools like fda_search_enforcement and fda_get_facility.

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?

Provides explicit usage instructions: 'Provide either recall_number (from fda_search_enforcement or fda_ires_enforcement) OR firm (+ optional product) to resolve the best-matching recall automatically.' It also names related tools for next steps ('Related: fda_get_facility, fda_inspections, fda_compliance_actions'), making when-to-use and alternatives clear.

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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