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Limecooler

fda-mcp

by Limecooler

search_fda

Search 21 OpenFDA datasets for specific records on adverse events, drug labels, device submissions, recalls, and more.

Instructions

Search any of the 21 OpenFDA datasets. Returns individual records.

When to use: Finding specific records — adverse events, drug labels, device submissions, recalls, etc. For aggregation/statistics, use count_records instead. If unsure which fields to search, call list_searchable_fields first.

Args: dataset: Which FDA dataset to search. Options by category:

    Drug:
      drug_adverse_events — FAERS adverse event reports
      drug_labels — SPL drug labeling (package inserts)
      drug_ndc — National Drug Code directory
      drug_approvals — NDA/ANDA/BLA approval history (Drugs@FDA)
      drug_recalls — Drug recall enforcement reports
      drug_shortages — Drug shortage reports

    Device:
      device_adverse_events — MDR adverse event reports
      device_510k — 510(k) clearances, De Novo grants, and HDE submissions
      device_pma — PMA approvals and supplements
      device_classification — Product code classification database
        (submission_type_id: 1=510k, 2=PMA, 4=510k Exempt, 6=De Novo, 7=HDE)
      device_recalls — Device recall enforcement reports
      device_recall_details — Detailed device recall information
      device_registration — Facility registrations and product listings
      device_udi — Unique Device Identifier database
      device_covid19_serology — COVID-19 serology test performance

    Food:
      food_adverse_events — CAERS adverse event reports
      food_recalls — Food/cosmetic recall enforcement reports

    Other:
      historical_documents — FDA historical documents
      substance_data — Substance data (GSRS)
      unii — Unique Ingredient Identifier codes
      nsde — NDC SPL Data Elements

search: OpenFDA query string. Quote string values and use + for spaces.
limit: Max results to return (default 10, max 100).
skip: Number of results to skip for pagination.
sort: Sort field and direction (e.g., "report_date:desc").

Examples: Drug adverse events: dataset="drug_adverse_events", search='patient.drug.openfda.brand_name:"ASPIRIN"' Drug labels: dataset="drug_labels", search='openfda.brand_name:"LIPITOR"' Device 510(k) clearances: dataset="device_510k", search='device_name:"pulse+oximeter"' De Novo grants (also in device_510k): dataset="device_510k", search='decision_code:"DENG"+AND+advisory_committee:"DE"' Food recalls: dataset="food_recalls", search='classification:"Class I"' Device classification lookup: dataset="device_classification", search='device_name:"oximeter"+AND+device_class:2'

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
skipNo
sortNo
limitNo
searchYes
datasetYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries full burden. It discloses that it returns individual records, not aggregates, and details query syntax with quoting and + for spaces, plus pagination and sorting behavior. It could mention rate limits or error handling, but the core behavior is transparent.

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?

The description is long but dense, with clear headers and practical examples. The dataset list is necessary given 21 enum values, and every section earns its place. Slightly verbose but not wasteful.

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?

Covers all essential aspects: purpose, usage guidance, parameter semantics, and examples. Since an output schema exists, not detailing return values is fine. This is a complete description for a complex tool with many dataset options.

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?

The description adds extensive meaning beyond the schema: dataset options are categorized and explained, search syntax is specified (quote strings, + for spaces), limit has default and max, skip is for pagination, and sort has a format example. This fully compensates for the 0% schema description coverage.

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?

Description clearly states it searches any of 21 OpenFDA datasets and returns individual records. It distinguishes from siblings by explicitly pointing to count_records for aggregation and list_searchable_fields for field guidance.

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?

Provides an explicit 'When to use' section, stating it's for finding specific records and directing users to count_records for statistics and list_searchable_fields if unsure of fields. This gives clear when-to-use and alternatives guidance.

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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