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lzinga

US Government Open Data MCP

fda_animal_events

Read-only

Search over 1.3 million animal drug adverse event reports to identify safety issues across species, drugs, reactions, and outcomes.

Instructions

Search animal/veterinary adverse event reports (1.3M+ reports). Reports of drug side effects in animals — dogs, cats, horses, cattle, etc. Each report has: animal info (species, breed), drugs, reactions (VEDDRA terms), outcomes. Note: Some fields may contain 'MSK' (masked) values for privacy.

Example searches:

  • 'animal.species:"Dog"' — dog events

  • 'original_receive_date:[20200101+TO+20231231]' — events in date range

  • 'serious_ae:true' — serious adverse events only

Count fields: animal.species.exact, primary_reporter.exact, serious_ae

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
searchNoOpenFDA search query. Examples: 'field:value', 'field:"Exact Phrase"', 'field:[20200101+TO+20231231]', '_exists_:field'. Combine with '+AND+', '+OR+', '+NOT+'.
limitNoMax results (default 10, max 100)
Behavior3/5

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

Annotations already declare readOnlyHint=true, so the read-only nature is covered. The description adds that some fields may contain 'MSK' (masked) for privacy, which is helpful. However, it does not disclose other behaviors like rate limits or required permissions, which are not covered by annotations either.

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 well-structured, starting with a one-line purpose, then explaining report contents, and ending with example queries and count fields. It is front-loaded and informative, though slightly longer than necessary.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description partially explains return values (animal info, drugs, reactions, outcomes) and provides example fields. However, it lacks a complete output structure description, which is adequate but not fully compensating for the missing output schema.

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%, so baseline is 3. The description adds value by providing domain-specific search examples (e.g., 'animal.species:"Dog"') and listing count fields (e.g., animal.species.exact), which go beyond the schema's generic query syntax.

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 it searches for animal/veterinary adverse event reports, a specific resource distinct from human drug events. It provides the dataset size (1.3M+ reports) and examples of search queries, making the tool's purpose unambiguous, especially given sibling tools like fda_drug_events for human drugs.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for animal adverse events but does not explicitly state when to use this tool vs alternatives (e.g., fda_drug_events for human events). It provides no when-not-to-use guidance, leaving the agent to infer from the 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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