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FDA Data MCP

by io.github.medley

Manufacturing Risk Summary

fda_manufacturing_risk_summary
Read-onlyIdempotent

Build a manufacturing and compliance summary for one company using FDA facilities, inspections, warning letters, OII records, import-risk signals, debarments, and recalls. Use this when you want the company-level picture first, then follow the linked granular tools for deeper inspection.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
companyYesCompany name to summarize
verboseNoWhen false (default), suppress the inline alias dump and collapse per-dataset data_freshness to a single top-level as_of. Set true for the full block.
evidence_limitNoMax recent records to return per evidence section
facility_limitNoMax facilities to return

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnly and idempotent hints, establishing a safe operation. The description adds value by disclosing the data sources aggregated and the summary-centric behavior, which goes beyond annotations. No contradictions detected.

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 two sentences, front-loaded with the core purpose and immediately followed by usage guidance. No wasted words; every phrase contributes to clarity.

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 complex aggregation tool with no output schema, the description adequately covers the data sources and the intended use case. It does not describe the return format, but the listed data sources give a concrete sense of the summary content. Slight gaps remain about behavior on unknown companies or result shape, but overall it is sufficient.

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%, so the schema fully documents all four parameters. The description adds no additional parameter context, matching the baseline expectation of 3 when the schema carries the load.

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 a specific action ('Build a manufacturing and compliance summary') and resource ('one company'), listing the data sources involved and differentiating from granular tools by emphasizing the 'company-level picture first'. It effectively positions itself as a high-level aggregation tool.

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 offers explicit usage context: 'Use this when you want the company-level picture first' and advises following 'linked granular tools' for deeper inspection. It implies when not to use it (for detailed dives) and points to alternatives, though without naming specific sibling tools.

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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