FDA Data MCP

Discover Subsidiaries

fda_suggest_subsidiaries

Read-onlyIdempotent

Discover subsidiary and related company names using FDA datasets first, then supplement with external corporate hierarchy sources (SEC EDGAR Exhibit 21 and GLEIF) when available. Costs 2 credits. Returns FDA name candidates, evidence-backed company-record suggestions, EDGAR subsidiaries, GLEIF subsidiaries, existing aliases, and facility coverage stats. The coverage.unlinked_feis count indicates how many facilities may be missing from the current alias set. The workflow is conservative and explainable: it validates candidates against FDA company records instead of auto-linking them. Note: EDGAR and GLEIF may lag recent acquisitions or divestitures, so missing external results do not rule out FDA-visible subsidiaries. Recommended workflow: 1. fda_suggest_subsidiaries, 2. fda_link_subsidiaries for distinct child companies or fda_save_aliases for true same-company variants, 3. fda_manufacturing_risk_summary or fda_search_family_facilities. Related: fda_link_subsidiaries (persist explicit family links), fda_save_aliases (persist same-entity names), fda_manufacturing_risk_summary (family-aware company rollup), fda_search_family_facilities (family-aware FEI search).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`cik`	No	SEC CIK (optional, auto-resolved if omitted)
`company`	Yes	Company name to analyze

TDQS

A4.7/5.0

Behavior5/5

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

The description adds substantial context beyond the annotations (readOnly, openWorld, idempotent): it states the 2-credit cost, enumerates the return payload (FDA candidates, EDGAR, GLEIF, aliases, facility coverage stats), explains the meaning of coverage.unlinked_feis, and discloses the conservative, explainable validation workflow. This exceeds what the annotations convey.

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 well-structured and information-dense: it opens with the core purpose, then covers cost, return items, a key metric, a caveat about external sources, and a recommended workflow. Every sentence adds value, and the workflow list improves scannability without padding.

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?

Despite having no output schema, the description fully compensates by listing the return categories, explaining an important field, and giving usage context. It covers cost, limitations, and follow-up tools, making it comprehensive for a tool with only two parameters and no output schema.

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%: both 'company' and 'cik' already have descriptive text. The description does not add further parameter-specific details beyond the schema, but it does mention the auto-resolution of CIK in the schema. Baseline of 3 is appropriate given complete 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 begins with a specific verb and resource: 'Discover subsidiary and related company names' and explains the multi-source approach (FDA datasets, then SEC EDGAR and GLEIF). It clearly distinguishes itself from sibling tools by naming related tools like fda_link_subsidiaries and fda_save_aliases.

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?

Provides explicit usage guidance: a numbered recommended workflow (fda_suggest_subsidiaries → fda_link_subsidiaries or fda_save_aliases → risk summary or family search), and clarifies when external sources may lag. It also contrasts with related tools, making it clear when to use this tool vs alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All FDA Data MCP tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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

Need Help?