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by io.github.blackboxfoundry

fda_drug_adverse_events

Read-onlyIdempotent

FDA Adverse Event Reporting System (FAERS) reports for a specific drug. Each result describes a reported adverse reaction including patient demographics, reactions, outcome, and seriousness. Used for pharmacovigilance and post-market safety analysis.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
drugYesDrug name (brand or generic) to query FAERS for. Example: 'Lipitor' or 'atorvastatin'.
limitNoMaximum rows to return (default 25, max 100).
end_dateNoInclusive ISO date upper bound (YYYY-MM-DD).
reactionNoOptional MedDRA-preferred-term reaction filter (e.g. 'headache', 'nausea', 'liver injury').
start_dateNoInclusive ISO date lower bound (YYYY-MM-DD).
serious_onlyNoIf true, only return serious adverse events (death, hospitalization, life-threatening, disability).

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare the tool read-only, idempotent, and non-destructive, so the description has a lower burden here. It adds useful context about result contents (demographics, reactions, outcome, seriousness), but it does not disclose operational details like pagination behavior, data caveats, or FAERS reporting limitations.

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 two concise sentences with no filler. It front-loads what the tool returns and then adds context, making it easy to scan and understand quickly.

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?

There is no output schema, so the description's note that each result includes demographics, reactions, outcome, and seriousness partially compensates. The schema covers all parameter constraints and filters. The only missing pieces are minor caveats about FAERS data quality or response pagination, which are not critical for invoking this tool correctly.

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?

The input schema has 100% parameter description coverage, so the schema already documents all six parameters and their semantics. The description adds high-level context but no additional parameter meaning beyond what the schema provides.

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 identifies the resource as FAERS adverse-event reports for a specific drug, which matches the tool name and differentiates it from siblings like fda_drug_lookup and fda_drug_recalls. It also describes what each result contains, leaving no ambiguity about the tool's domain.

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 gives a clear use context: pharmacovigilance and post-market safety analysis for a specific drug. It does not explicitly name alternatives or when not to use the tool, but the stated purpose is concrete enough for an agent to choose it correctly among many FDA-related siblings.

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

B3.2/5.0
Disambiguation2/5

Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.

Naming Consistency3/5

The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.

Tool Count1/5

290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.

Completeness4/5

For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.

Resources