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lzinga

US Government Open Data MCP

fda_drug_counts

Read-only

Count FDA adverse event reports by reaction, drug brand, generic name, country, or date. Filter results to identify trends in drug safety data.

Instructions

Aggregate/count FDA drug adverse event data by any field. For counting other endpoints, use fda_count instead.

Common count fields:

  • 'patient.reaction.reactionmeddrapt.exact' — most common adverse reactions

  • 'patient.drug.openfda.brand_name.exact' — most reported drug brands

  • 'patient.drug.openfda.generic_name.exact' — most reported generic names

  • 'receivedate' — reports over time

  • 'primarysource.reportercountry.exact' — reports by country

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
count_fieldYesField to count by. Use '.exact' suffix for full phrase counts. E.g. 'patient.reaction.reactionmeddrapt.exact'
searchNoOptional search filter, e.g. 'patient.drug.openfda.brand_name:aspirin'
limitNoMax count results (default 10)
Behavior3/5

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

Annotations already provide readOnlyHint, indicating safe read operation. Description adds common count fields as examples but does not disclose pagination or response format. No contradiction with annotations.

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?

Extremely concise: one sentence for main action, one for differentiation, then bullet list of examples. Front-loaded and no wasted words.

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?

No output schema, but the tool's purpose (aggregate/count) implies the return is count data. Description is sufficient for a counting tool, though could mention response structure.

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

Parameters4/5

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

Schema coverage is 100%, and the schema description for count_field is clear. However, the description adds significant value by listing common count fields with examples, aiding agent understanding 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?

Description clearly states the tool aggregates/counts FDA drug adverse event data by any field, using specific verbs and resource. It differentiates from sibling fda_count by specifying 'for counting other endpoints'.

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?

Explicitly tells when to use this tool vs. alternative: 'For counting other endpoints, use fda_count instead.' Provides clear context and exclusions.

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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