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Search Drug Labels

fda_drug_labels
Read-onlyIdempotent

Search FDA Structured Product Labeling (SPL) data — full drug package inserts. Filter by drug name, manufacturer, application number, or specific label section (e.g., indications_and_usage, warnings, adverse_reactions, boxed_warning). Returns complete label text for matching sections. Related: fda_search_drugs (application-level data), fda_search_ndc (NDC product details).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoResult limit
offsetNoResult offset
sectionNoSpecific label section to return (e.g. indications_and_usage, warnings, adverse_reactions)
drug_nameNoBrand or generic drug name (fuzzy match)
manufacturerNoManufacturer name (fuzzy match)
application_numberNoNDA or ANDA application number

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already establish read-only and idempotent behavior. The description adds that the tool returns 'complete label text for matching sections' and provides concrete section examples, which is useful context beyond the annotations. No contradiction found.

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 four sentences, each serving a distinct purpose: scope, filters, return behavior, and related tools. It is front-loaded with the core action and contains no filler or repetition.

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?

With no output schema, the description provides the essential return characteristic ('complete label text for matching sections') and enough filter/alternative context for a search tool. Pagination and limits are left to the schema, which is reasonable for a read-only operation.

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 already covers all 6 parameters with descriptions, so the baseline is 3. The tool description summarizes the filter types and adds one extra section example ('boxed_warning'), but does not substantially enrich understanding of parameter behavior beyond the schema.

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 opens with 'Search FDA Structured Product Labeling (SPL) data — full drug package inserts', clearly identifying the action and resource. It distinguishes itself from siblings by naming fda_search_drugs and fda_search_ndc with their different scopes.

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 clear context by stating this tool returns full label sections and names related tools with their distinct purposes, implying when to choose an alternative. It does not explicitly state a 'when not to use' condition, but the alternative scopes effectively communicate that.

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

A4.1/5.0
Disambiguation4/5

Most tools have distinct purposes with clear boundaries, such as fda_search_drugs for drug applications and fda_search_510k for device clearances. However, some overlap exists, like fda_device_udi and fda_device_udi_lookup both querying UDI data, which could cause confusion despite differences in scope.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern with a clear fda_ prefix, using descriptive verbs like search, get, list, and link. This uniformity makes the set predictable and easy to navigate, with no deviations in naming style.

Tool Count2/5

With 48 tools, the count is excessive for a single server, making it overwhelming and difficult for agents to manage. While the domain is broad (FDA data), the toolset feels bloated with many specialized or overlapping tools that could be consolidated.

Completeness5/5

The toolset provides comprehensive coverage of FDA data domains, including drugs, devices, inspections, compliance, recalls, and facilities. It supports full CRUD-like operations (e.g., search, get, link, save) and lifecycle workflows, with no obvious gaps for the intended purpose.

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