FDA Data MCP

Search Recalls

fda_search_enforcement

Read-onlyIdempotent

Search FDA enforcement actions (recalls) for drugs, devices, and food across all companies. Filter by company name (fuzzy match), recall classification (Class I=most serious/Class II/Class III), date range, or status (Ongoing/Terminated). Returns recall details including product description, reason, and distribution pattern. Related: fda_recall_facility_trace (trace a recall to its manufacturing facility by recall_number), fda_ires_enforcement (iRES recall data with cross-references), fda_device_recalls (device-specific recall data).

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Max results to return (1-500)
`offset`	No	Result offset for pagination
`status`	No	Recall status
`company`	No	Company or firm name (fuzzy search)
`to_date`	No	End date for report_date range (YYYY-MM-DD)
`from_date`	No	Start date for report_date range (YYYY-MM-DD)
`classification`	No	Recall classification severity

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, so the safety profile is covered. The description adds useful behavioral details: fuzzy matching for company, classification severity ('Class I=most serious'), and return content ('product description, reason, distribution pattern'). It does not contradict 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?

The description is compact and front-loaded: first sentence defines scope, second lists filters, third describes return details, fourth provides sibling alternatives. Every sentence earns its place with no 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?

Given 7 optional parameters, no output schema, and a broad sibling list, the description covers purpose, filters, return content, and relationships to related tools. It provides sufficient context for correct selection and invocation without external documentation.

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 the baseline is 3. The description enhances parameter understanding by explaining fuzzy match behavior for 'company', clarifying classification levels ('Class I=most serious/Class II/Class III'), and grouping date range/status as filter categories, adding meaning beyond the schema.

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 verb ('Search'), resource ('FDA enforcement actions/recalls'), and scope ('for drugs, devices, and food across all companies'). It distinguishes itself from siblings by naming alternative tools like fda_recall_facility_trace and fda_device_recalls with their specific purposes.

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 lists filter criteria (company, classification, date range, status) and includes a 'Related:' section that tells when to use alternative tools (e.g., trace to facility, iRES data, device-specific recalls). This provides clear when-to-use guidance and exclusions.

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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