FDA Data MCP

Search Inspections

fda_inspections

Read-onlyIdempotent

Search FDA inspection history from the Compliance Dashboard (not available in openFDA API). Filter by company name (fuzzy match), FEI number, classification (NAI=No Action Indicated, VAI=Voluntary Action Indicated, OAI=Official Action Indicated — most serious), state, country, city, or date range. Date filters apply to inspection_end_date. OAI inspections typically lead to warning letters. Related: fda_citations (specific CFR violations from inspections by FEI), fda_compliance_actions (warning letters following OAI inspections by FEI).

Input Schema

TableJSON Schema

Name	Required	Description
`city`	No	City name (exact match)
`limit`	No	Max results to return (1-500)
`state`	No	State code (e.g., CA, NY)
`country`	No	Country code (e.g., US, DE)
`date_to`	No	End date for inspection_end_date range (YYYY-MM-DD)
`date_from`	No	Start date for inspection_end_date range (YYYY-MM-DD)
`fei_number`	No	FDA Establishment Identifier (FEI number)
`company_name`	No	Company name (fuzzy match)
`classification_code`	No	Inspection classification code

TDQS

A4.4/5.0

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 description builds on that by adding source context (Compliance Dashboard vs openFDA), classification semantics (NAI/VAI/OAI severity), and the behavior that date filters apply to inspection_end_date. This adds useful behavioral nuance beyond the annotations without contradicting them.

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 with the main purpose. It wastes no words, packs in filter details, classification explanations, and related-tool pointers in three sentences. Every sentence earns its place.

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

Completeness4/5

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

For a read-only search tool with no output schema, the description covers the data source, filter types with special semantics, and related follow-up tools. It does not mention pagination or response format, but those are partly addressed by the limit parameter in the schema. The description is sufficiently complete for an agent to decide to invoke it.

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?

The schema covers 100% of parameter descriptions, so the description's extra semantic details are valuable: company_name is a fuzzy match, classification_code values are expanded with their meanings and relative severity, and date filters are explicitly tied to inspection_end_date. This elevates the description above merely repeating 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 opens with a specific verb and resource: 'Search FDA inspection history from the Compliance Dashboard.' It clearly distinguishes this tool from siblings by noting it is not available in the openFDA API and by naming related tools (fda_citations, fda_compliance_actions) for different purposes.

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 gives clear context for when to use this tool: to search inspection history with various filters. It also provides alternatives via 'Related: fda_citations... fda_compliance_actions...' and explains that OAI inspections typically lead to warning letters, which helps the agent decide whether to use this tool or fda_compliance_actions. It does not explicitly state exclusions (e.g., 'use X instead when...'), so it is not a 5.

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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