FDA Data MCP

Search 510(k) Clearances

fda_search_510k

Read-onlyIdempotent

Search FDA 510(k) clearances across all companies. Filter by company name (fuzzy match), product code, decision code (e.g., SESE=substantially equivalent), clearance type (Traditional, Special, Abbreviated), and date range. Returns clearance number (K-number), applicant, device name, decision date, and product code. Related: fda_device_class (product code details and classification), fda_product_code_lookup (cross-reference a product code across 510(k) and PMA), fda_search_pma (PMA approvals for higher-risk devices).

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
`decision_code`	No	Decision code (e.g., SESE, SESD)
`clearance_type`	No	Clearance type (e.g., Traditional, Special)

TDQS

A4.7/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, covering the safety profile. The description adds useful behavioral context beyond these annotations, such as the fuzzy-match behavior for company names and the specific return fields (K-number, applicant, device name, decision date, product code). While it doesn't mention pagination or rate limits, the annotations reduce the burden, so a 4 is appropriate.

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 a clear structure: purpose in the first sentence, filters/returns in the second, and related tools in the third. Every sentence earns its place, and there is no redundancy or irrelevant detail.

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 search tool with 8 parameters and no output schema, the description adequately covers the main filters and return values, and it points to related tools for further context. The schema fills in parameter specifics, making the description sufficiently complete without over-explaining.

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?

With 100% schema description coverage, the baseline is 3. The description adds extra semantic meaning by explaining decision code examples (e.g., SESE=substantially equivalent) and expanding clearance type examples to include 'Abbreviated', which enriches the parameter context beyond the schema. This justifies a score above baseline.

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 action ('Search') and the resource ('FDA 510(k) clearances') with an explicit scope ('across all companies'). It enumerates specific filters and return fields, and distinguishes itself from sibling tools by naming related tools and their purposes, making its unique role unambiguous.

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 provides explicit guidance on tool selection by listing related tools with their purposes: fda_device_class for product code details, fda_product_code_lookup for cross-referencing, and fda_search_pma for higher-risk devices. This effectively communicates when to use this tool versus alternatives.

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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