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KeyVex

get_drug_adverse_events

Read-only

Returns FDA FAERS drug adverse-event reports — every adverse-event / medication-error report submitted to FDA (~20M, 2004→present, growing ~2M/yr). LIVE passthrough to openFDA: results reflect FDA's current data and total_count is openFDA's authoritative count for the filtered query (the results array is just the requested page). Use this when the user asks about: safety signals on a drug, adverse events by reaction type, death/hospitalization outcome counts for a product, a manufacturer's adverse-event footprint, or to pair a safety-signal trend with recalls, approvals, or insider activity. count_by returns TOP TERMS + COUNTS instead of records (e.g. count_by:'reaction' with drug:'ozempic' → the most-reported reactions for that drug) — the right first move for 'what are the side effects of X' questions; follow with a record query for detail. Records are openFDA's fields verbatim: deeply nested (patient.drug[] with openFDA annotations, patient.reaction[]), 5-15KB each — keep limit small. Matching: drug matches brand name, generic name, or the verbatim reported product as a PHRASE ('ozempic', 'semaglutide'); reaction is a MedDRA term phrase ('myocardial infarction'); serious=true filters to reports with a serious outcome; outcome picks one specific flag (death, hospitalization, …). since/until window on receivedate (when FDA received the report). CRITICAL honesty note (FDA's own): FAERS reports are UNVERIFIED and establish NEITHER causation NOR incidence — anyone can report, duplicates exist, reporting is stimulated by publicity, and there is no denominator (prescriptions dispensed). Counts are a reporting signal, not a risk measure. Surface this caveat when presenting counts. Pure-publisher posture: FDA's records as published, parsed by KeyVex — no derived safety scores.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
drugNoDrug name phrase — brand ('ozempic'), generic ('semaglutide'), or reported product name.
skipNoPagination offset (openFDA hard cap 25,000). Not used in count_by mode.
limitNoRecords per page (default 10, max 100 — records are heavy). In count_by mode: facet rows (default 25, max 1000).
sinceNoFDA receive-date lower bound (YYYY-MM-DD inclusive).
untilNoFDA receive-date upper bound (YYYY-MM-DD inclusive).
countryNoCountry where the event occurred (ISO-2, e.g. 'US').
outcomeNoOne specific seriousness outcome flag.
seriousNotrue = serious reports only (death, hospitalization, disability, …); false = non-serious only.
count_byNoFacet mode: return top terms + counts instead of records (reaction | drug | brand | manufacturer | country).
reactionNoMedDRA reaction term phrase (e.g., 'pancreatitis', 'myocardial infarction').
manufacturerNoManufacturer name phrase (openFDA annotation, e.g. 'novo nordisk').

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already cover the safety profile (readOnlyHint, openWorldHint, destructiveHint=false), yet the description adds substantial context: a LIVE passthrough, the fact that total_count is authoritative while the results array is only the requested page, that records are 5-15KB and limit should stay small, and FDA's own caveat that reports are unverified with no denominator. This is genuinely additive beyond structured data.

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-loaded with what the tool returns and well-organized into matching/filter/caveat blocks. It is dense and long (~250 words) with mild repetition in the honesty note, but nearly every sentence earns its place for an 11-param, output-schema-less tool.

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 return-value burden and does so: records are openFDA fields verbatim, deeply nested (patient.drug[], patient.reaction[]), 5-15KB, while count_by returns top terms plus counts and total_count is the authoritative filtered count. Nothing an agent needs to call and interpret it is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is already 100% (baseline 3), but the description exceeds it with matching semantics not in the schema: drug matches brand/generic/verbatim product as a PHRASE, reaction is a MedDRA phrase, serious vs. outcome are distinguished, since/until window on receivedate, and a concrete count_by example. It meaningfully deepens parameter understanding.

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 ('Returns FDA FAERS drug adverse-event reports') with scope (~20M reports, 2004→present, live passthrough to openFDA). It explicitly distinguishes itself from adjacent tools by describing its role alongside recalls, approvals, and insider activity, so an agent can place it among the ~65 siblings.

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

Usage Guidelines5/5

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

Gives an explicit 'Use this when the user asks about' list (safety signals, reactions, outcome counts, manufacturer footprint) and goes further with a decision rule: count_by is 'the right first move' for side-effect questions, followed by a record query for detail. This is exactly the when/when-not/alternatives guidance the dimension asks for.

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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