FDA Data MCP

Search Inspection Observation Summary

fda_inspection_observation_summary

Read-onlyIdempotent

Search FDA's official annual inspection-observation summary spreadsheets. This is aggregate Form 483 trend data by product area and citation frequency, not a full company-level 483 corpus. Use it to see which observation areas appear most often in Drugs, Devices, Foods, and other FDA program areas.

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Max results to return (1-500)
`offset`	No	Result offset for pagination
`cite_id`	No	FDA citation identifier from the spreadsheet
`keyword`	No	Full-text query across short and long citation descriptions
`fiscal_year`	No	Fiscal year of the spreadsheet
`product_area`	No	Product or program area (e.g. Drugs, Devices, Foods)
`reference_number`	No	Regulatory citation reference number (e.g. 21 CFR 211.192)

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 safety profile is clear. The description adds valuable context that the data is aggregate trend data by product area and citation frequency, which sets expectations for the type of results. However, it does not disclose return format or pagination behavior, which could have been useful given the lack of an output schema.

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 three concise sentences that are front-loaded with the primary action ('Search...'). Each sentence contributes distinct value: the resource, the data nature, and the use case. There is no redundancy or filler.

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?

Given there is no output schema, the description reasonably explains the data scope and purpose, including the aggregate nature and product areas. It does not detail the exact result structure, but for a simple search tool with clear annotations and schema, the description is sufficiently complete to set expectations.

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 description coverage is 100% with all 7 parameters clearly described. The description does not add additional parameter-specific meaning beyond schema, but it does reinforce the product_area concept by mentioning Drugs, Devices, Foods. This aligns with the baseline score of 3 for high schema coverage.

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 searches FDA's official annual inspection-observation summary spreadsheets, with a specific verb and resource. It further distinguishes the tool by noting it provides aggregate Form 483 trend data by product area and citation frequency, not a full company-level 483 corpus, which effectively separates it from sibling tools like fda_inspections.

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?

The description gives explicit usage guidance: 'Use it to see which observation areas appear most often' provides a clear when-to-use, and 'not a full company-level 483 corpus' provides an explicit when-not-to-use. While it does not name an alternative tool, the stated exclusions are sufficient to guide tool selection.

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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