Pet Data Station

get_ade_stats

FDA animal-drug adverse-event (ADE) stats per active ingredient (openFDA CVM report database, 68 compiled ingredients).

ingredient: active-ingredient name or substring, e.g. 'afoxolaner', 'carprofen', 'fluralaner'. species: optional filter, e.g. 'Dog', 'Cat', 'Horse' — restricts to ingredients reporting that species and returns only that species' count. Returns structured summary stats (total reports, species breakdown, top VEDDRA reactions) parsed from C1-verified topics — report-level aggregates, not additive, not causation. Full citation-rich text via get_topic_context(topic_id).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No
`species`	No
`ingredient`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.7/5.0

Behavior5/5

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 discloses key behaviors: data source ('C1-verified topics'), output semantics ('report-level aggregates, not additive, not causation'), and species filter behavior. This exceeds typical transparency and helps set expectations.

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 well-structured with a purpose statement, parameter details, output description, and a pointer to an alternative. Each sentence adds value, and the layout makes key details easy to scan.

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?

For a read-only stats tool with no annotations, the description provides thorough context: data source, scope (68 ingredients), output contents, caveats, and a clear route to deeper data. The presence of an output schema further covers return details, so no critical context is missing.

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 description explains 'ingredient' and 'species' with examples and filter semantics, but the 'limit' parameter is not described at all. Since schema coverage is 0%, the description must compensate for all parameters, but it misses one out of three, leaving ambiguity about what 'limit' controls.

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 provides 'FDA animal-drug adverse-event (ADE) stats per active ingredient' from a specific database, with a specific verb ('get stats') and resource. It distinguishes itself from siblings by focusing on aggregated ADE stats, not topics, recalls, or nutrient data.

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?

It explicitly directs users to 'get_topic_context(topic_id)' for full citation-rich text, implying this tool is for structured summary stats instead. This provides an explicit alternative and clarifies when to use this tool vs. a sibling.

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

A4.2/5.0

Disambiguation5/5

Each tool targets a distinct resource or action: breed bans, nutrient values, nutrient comparisons, ADE stats, recalls, topic search, topic context, and feedback. Even the two nutrient tools (get_nutrient_value vs. compare_nutrient_standards) are clearly separated by single-value vs. multi-standard comparison, and search_pet_topics vs. get_topic_context are search-vs-retrieve.

Naming Consistency4/5

All tool names use snake_case and a verb_noun structure (check_, compare_, get_, search_, submit_). However, the verbs vary (check, compare, get, search, submit) rather than following a single verb family, and three tools start with 'get' while two start with 'search', which is slightly less uniform than a fully consistent pattern but still predictable.

Tool Count5/5

With 8 tools, the server is well-scoped. The set covers core retrieval operations (search, get context), domain-specific queries (breeds, nutrients, ADE, recalls), and a feedback mechanism. Each tool earns its place without redundancy or bloat.

Completeness4/5

The surface covers the apparent domain: searchable topics, detailed context retrieval, and specialized lookups for common pet regulatory questions. A minor gap is that there is no explicit tool to list all available jurisdictions, standards, or categories, but search_pet_topics with category filters and the feedback tool mitigate this. No critical dead ends.

Resources

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