FDA Data MCP

Search PMA Approvals

fda_search_pma

Read-onlyIdempotent

Search FDA Pre-Market Approval (PMA) records across all companies. PMA is required for high-risk (Class III) devices. Filter by company name (fuzzy match), product code, and date range. Returns PMA number, applicant, trade name, decision date, and product code. Related: fda_device_class (product code details), fda_search_510k (510(k) clearances for lower-risk devices), fda_product_code_lookup (cross-reference a product code).

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Max results to return (1-500)
`offset`	No	Result offset for pagination
`company`	No	Company name (fuzzy search)
`to_date`	No	End date for decision_date range (YYYY-MM-DD)
`from_date`	No	Start date for decision_date range (YYYY-MM-DD)
`product_code`	No	Device product code

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already provide readOnlyHint=true, idempotentHint=true, and openWorldHint=false, so the safety profile is covered. The description adds useful behavioral details: fuzzy company matching, date-range filtering, and the specific fields returned (PMA number, applicant, trade name, decision date, product code). This goes 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?

Three sentences, front-loaded with the core action, then key filters, return fields, and related tools. No wasted words; each sentence 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?

The description fully covers a search tool: it states scope, provides regulatory context, lists all relevant filters, names return fields, and points to related tools. With good annotations and full schema, nothing essential is missing.

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 clear descriptions for all 6 parameters. The description reiterates 'company name (fuzzy match), product code, and date range' but does not add substantial new meaning beyond the schema. Baseline 3 is appropriate since the schema does the heavy lifting.

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+resource+scope: 'Search FDA Pre-Market Approval (PMA) records across all companies.' It clearly distinguishes this from siblings like fda_search_510k by naming PMA and its high-risk (Class III) context, and explicitly lists 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?

It explicitly states when to use PMA ('high-risk (Class III) devices') and points to fda_search_510k for lower-risk devices. It also names fda_device_class and fda_product_code_lookup as complementary lookups, giving clear alternatives and context.

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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