UK Data for Agents

fda_drug_side_effects_reported

Read-onlyIdempotent

What side effects do people report for this medicine? Ranks the reactions most often named in reports to the FDA Adverse Event Reporting System (FAERS) for a medicine, with report counts. Popular with patients, journalists and pharmacovigilance work. Source: U.S. FDA openFDA FAERS. Reports are unverified, include every drug the patient was taking, and do not show the medicine caused the reaction or how common it is; label side effect rates are a different measure. (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_drug_side_effects_reported"
  }
}

Input Schema

Name	Required	Description	Default
`name`	No	string, required, brand or generic name, e.g. metformin
`limit`	No	integer, optional, number of reactions to rank, 1-50, default 10

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 5, 2026

TDQS

A4.4/5.0

Behavior5/5

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

Annotations already cover read-only, idempotent, open-world and non-destructive behavior, and the description adds substantial context beyond that: provenance (openFDA FAERS), that reports are unverified and polypharmacy-contaminated, that they do not establish causation or population frequency, and the 1-credit cost. That is exactly the behavioral caveat an agent needs before quoting results.

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?

It opens with the user-facing question, then the mechanism, then caveats, then cost — well front-loaded with no dead sentences. The caveat sentence is long and dense, but every clause (unverified, all drugs included, no causation, distinct from label rates) carries real information.

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?

For a two-parameter read-only lookup with no output schema, the definition covers purpose, provenance, limitations, and cost. Nothing an agent needs to call it correctly or interpret its output responsibly is missing.

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 (name, limit) are already documented with examples and ranges. The description implies the medicine input ('for this medicine') and report counts but adds no syntax, format, or naming detail beyond the schema, 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?

The description states a specific verb and resource: it RANKS the reactions most often named in FAERS reports, with report counts. This is clearly distinguishable from siblings like fda_drug_shortages (shortages) and fda_ndc_lookup (product codes), so an agent can route without opening a schema.

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?

It gives context for use ('popular with patients, journalists and pharmacovigilance work') and, importantly, scopes what the data is not. It stops short of naming an explicit alternative (e.g. clinical_trials or label data) for when the agent actually needs causal or prevalence rates, so it is clear but lacks explicit alternatives/exclusions.

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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