UK Data for Agents

fda_supplement_reactions

Read-onlyIdempotent

Counts the reactions most often named in reports to the FDA (CAERS) about a food, drink, dietary supplement or cosmetic brand, for example "CELSIUS", most frequent first. Helps people asking about energy drinks, supplements and protein powders see what has been reported, with the caveat that reports are unverified. Source: FDA CFSAN Adverse Event Reporting System via openFDA. Counts are of reports mentioning each reaction, not proven side effects or rates, and small brands may have few or no reports. (cost: 1 credit)

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "fda_supplement_reactions"
  }
}

Input Schema

Name	Required	Description	Default
`brand`	No	string, required, brand or product name as reported, e.g. "CELSIUS"
`limit`	No	integer, optional, 1-50, number of reactions to return, default 20

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 5, 2026

TDQS

A4/5.0

Behavior4/5

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

Annotations already declare read-only, idempotent, open-world and non-destructive, so the description's job is to add context — and it does: counts are of reports mentioning each reaction, not proven side effects or rates, reports are unverified, small brands may return little or nothing, and the call costs 1 credit. It does not describe result ordering failures or empty-result behavior beyond the small-brand warning.

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?

Front-loads what is counted and for what input, then layers the necessary caveats and the credit cost. A few clauses ("Helps people asking about…", the source attribution) could be trimmed, but nothing is genuinely wasted.

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?

With no output schema, the description carries the burden of describing results and does so — reaction names paired with report counts, sorted descending, subject to the limit. Combined with the unverified-report and small-brand caveats, an agent has everything needed to call it and interpret the response.

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 both parameters (brand, limit) are already documented in structured form. The description restates the example brand "CELSIUS" and the most-frequent-first ordering but adds nothing about the limit range or brand string matching (exact vs fuzzy), so baseline 3 applies.

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?

States a specific verb and resource: counts the reactions most often named in FDA CAERS reports for a named food, drink, supplement or cosmetic brand, returned most-frequent-first. The scope wording (food/drink/supplement/cosmetic brand) implicitly separates it from drug- and pet-oriented siblings such as fda_drug_side_effects_reported and fda_pet_drug_reactions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives concrete use cases (questions about energy drinks, supplements, protein powders) with the sharp caveat that reports are unverified and small brands may have few results. It never names an alternative tool or states when a different reaction/side-effect tool would be the better choice, so routing to siblings is left to inference.

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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