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lzinga

US Government Open Data MCP

fda_drug_counts

Count FDA drug adverse event reports by specific fields like reactions, drug names, dates, or countries to identify patterns and trends in medication 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?

No annotations are provided, so the description carries the full burden. It describes the aggregation behavior and provides common count fields as examples, which adds useful context. However, it lacks details on rate limits, authentication needs, error conditions, or the format of returned results, leaving gaps in behavioral understanding for a tool with no output schema.

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 front-loaded with the core purpose, followed by usage guidance and practical examples. Every sentence earns its place: the first states the action, the second provides sibling differentiation, and the list offers actionable field examples without redundancy. It is efficiently structured and appropriately sized.

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?

Given the tool's moderate complexity (3 parameters, 1 required), 100% schema coverage, and no output schema, the description does well by clarifying purpose, usage, and providing field examples. However, the lack of annotations and output schema means some behavioral aspects (e.g., result format, error handling) are not addressed, preventing a perfect score.

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?

The input schema has 100% description coverage, so the baseline is 3. The description adds value by providing concrete examples of common count fields (e.g., 'patient.reaction.reactionmeddrapt.exact') and clarifying the use of '.exact' suffix, which enhances understanding beyond the schema's generic parameter descriptions.

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 states the tool's purpose with a specific verb ('Aggregate/count') and resource ('FDA drug adverse event data'), and explicitly distinguishes it from its sibling 'fda_count' by specifying the data domain. It goes beyond a tautology by explaining the aggregation capability 'by any field'.

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?

The description provides explicit guidance on when to use this tool versus alternatives: it specifies 'For counting other endpoints, use fda_count instead,' clearly delineating the scope. This gives the agent direct instructions on tool selection based on the data domain.

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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