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LiveDataLink

by io.github.blackboxfoundry

fda_drug_lookup

Read-onlyIdempotent

Look up FDA drug label info by NDC code, brand name, or generic name. Returns indications, dosage, warnings, contraindications, mechanism, manufacturer, and DEA scheduling. Used for clinical decision support, pharmacy automation, drug-info chatbots.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum rows to return (default 25, max 100).
queryYesNDC code (e.g. '0002-1407'), brand name (e.g. 'Lipitor'), or generic name (e.g. 'atorvastatin').

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint: false, covering the safety profile. The description adds behavioral context by listing the specific return content (indications, dosage, warnings, contraindications, mechanism, manufacturer, DEA scheduling), which is valuable since there is no output schema. It does not mention rate limits or data freshness, but those are less critical for this read-only lookup.

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?

Two sentences with no filler. The first sentence states the core action, query types, and return fields; the second adds practical use cases. Every word contributes.

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 simple read-only lookup tool, the description is complete: it specifies the required query, the optional limit (via schema), the return contents, and the intended use cases. Annotations cover the safety profile, and the schema covers parameters, so nothing an agent needs to call it correctly 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%, with both parameters fully documented including examples for query (NDC, brand, generic) and default/max for limit. The description restates the query modes but adds no new semantic detail beyond the schema, so the baseline of 3 is appropriate.

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 uses a specific verb ('Look up') and resource ('FDA drug label info'), then enumerates the query modes (NDC, brand, generic) and the return fields (indications, dosage, warnings, etc.). This clearly distinguishes it from sibling tools like fda_drug_recalls or fda_drug_adverse_events, which address different FDA data.

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 lists concrete use cases (clinical decision support, pharmacy automation, drug-info chatbots) that implicitly indicate when this tool is relevant. It does not explicitly name alternatives or give exclusion criteria, but the context is clear enough among the 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