Govparse Government Data Gateway

fda_recalls_search

Which companies have an open or recent FDA drug/device recall? Search openFDA drug + device enforcement (recall) reports by recalling firm, product type (Drugs/Devices), classification (Class I/II/III), status, state, or initiation date (since). Returns the recalling company (entity-resolved where possible), classification, reason, product, and dates — a remediation and competitive-displacement trigger for quality and med-device sellers. openFDA public-domain records; observational. [price: $0.05/row]

Input Schema

TableJSON Schema

Name	Required	Description
`sort`	No	recall_initiation_date \| report_date :asc\|:desc. Default recall_initiation_date:desc.
`limit`	No	Max rows (default 25, cap 100).
`since`	No	Recall initiated on/after this date (YYYY-MM-DD).
`state`	No	Recalling-firm state code(s), CSV.
`offset`	No	Rows to skip.
`status`	No	Ongoing \| Terminated \| Completed (CSV).
`company`	No	Recalling firm — suffix/punctuation-insensitive.
`entity_id`	No	Resolved employer entity UUID.
`product_type`	No	Drugs \| Devices (CSV).
`classification`	No	Class I \| Class II \| Class III (CSV).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

AddedJul 24, 2026

TDQS

A4.1/5.0

Behavior3/5

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

No annotations provided, so description carries the burden. It mentions 'observational' and 'public-domain records', indicating read-only nature, but lacks details on pagination, data freshness, or rate limits. Adequate but not thorough.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is information-dense but well-structured, starting with a question and covering source, filters, outputs, and price. Slightly lengthy but each sentence adds value.

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 search tool with 10 parameters and no output schema, the description lists output fields, source, and pricing. It adequately prepares the agent for usage, though could detail return structure more.

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?

Parameters are fully covered in the schema (100% coverage). The description adds value by grouping filter types (e.g., 'by recalling firm, product type...') and summarizing the search capability, going beyond mere schema repetition.

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 it searches openFDA enforcement reports by recalling firm, product type, classification, etc., and lists the output fields. It distinguishes from sibling FDA tools like fda_approvals_drugs and fda_warningletters_search by focusing specifically on recalls.

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 implies when to use (for recall information) and lists available filters, but does not explicitly exclude cases or name alternatives. Context from sibling tool names provides differentiation.

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

A4.2/5.0

Disambiguation5/5

Each tool targets a distinct domain and specific action (e.g., FDA approvals vs clearances vs recalls; firmstanding business360 dossier vs search vs screen). Even overlapping concepts like 'business360' vs 'business360_lookup' are distinguished by input (UUID vs name+state). No two tools appear to do the same thing.

Naming Consistency5/5

All tools use a consistent lowercase snake_case pattern with domain prefix (e.g., fda_*, firmstanding_*, fmcsa_*, govcon_*). Action words (search, lookup, screen, feed, stats) follow predictable usage. The naming is uniform and easy to parse.

Tool Count4/5

38 tools is on the higher end but appropriate for a comprehensive government data gateway spanning multiple agencies and datasets. Each domain has a reasonable number of tools (e.g., FMCSA: 7, OFLC: 6). Could potentially be trimmed slightly, but overall well-scoped for the stated purpose.

Completeness5/5

The tool surface covers the major government data sources comprehensively: FDA (approvals, clearances, recalls), FMCSA (carrier census, safety, insurance, etc.), FSIS, DOJ/OFLC, OSHA/EPA/DOL enforcement, SEC insider filings, clinical trials, VA facilities/opportunities/vendors, and federal contracting. No obvious gaps for the stated gateway purpose.

Resources

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