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lzinga

US Government Open Data MCP

by lzinga

fda_food_adverse_events

Read-onlyIdempotent

Search FDA CAERS for adverse event reports on foods and dietary supplements to identify illnesses, allergic reactions, and injuries.

Instructions

Search FDA food adverse event reports (CAERS database). Reports of illnesses, allergic reactions, and injuries from foods and dietary supplements.

Example searches:

  • 'products.industry_name:"Dietary Supplements"' — supplement events

  • 'reactions:"hospitalization"' — events involving hospitalization

Count fields: reactions.exact, outcomes.exact, products.industry_name.exact

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results (default 10, max 100)
searchNoOpenFDA search query. Examples: 'field:value', 'field:"Exact Phrase"', 'field:[20200101+TO+20231231]', '_exists_:field'. Combine with '+AND+', '+OR+', '+NOT+'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv2026.9.14
    • addedInput schema / properties / limit / default
      Added value: +10
  2. Changed1 schema field changedv2026.4.11
    • removedInput schema / additionalProperties
      Removed value: -false
  3. First observedv2026.3.9

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, so the description has a low safety-disclosure burden. It adds useful behavioral context by giving CAERS-specific example queries and listing count fields such as 'reactions.exact' and 'products.industry_name.exact'.

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 compact and front-loaded: purpose statement, then example searches, then a count-fields hint. Every line earns its place, and there is no fluff or tautology.

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 two-parameter read-only search tool with a fully documented schema, the description is nearly complete: it gives domain context, realistic example queries, and count-field hints. A minor gap is the lack of any explicit note about the response shape, but the opening line makes clear it returns matching food adverse event reports.

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?

The schema already documents both parameters with 100% schema coverage, so the baseline is 3. The description adds value beyond the schema with realistic search examples and named count fields. The limit parameter needs no additional explanation beyond its schema description.

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 'Search FDA food adverse event reports (CAERS database)', giving a specific verb and resource. It names the domain — food and dietary supplements — and the report types, distinguishing it clearly from sibling tools like fda_drug_events, fda_device_events, and fda_food_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 provides clear context for when to use this tool: it is for food adverse event reports in the CAERS database, with example searches that narrow to supplements or hospitalization events. It does not explicitly name sibling tools as alternatives, so it stops just short of full when-not-to-use guidance.

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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