Search pet-food recall / enforcement data (FDA + EU RASFF + CN enforcement).
brand: brand/product keyword, e.g. 'diamond'; reason: hazard keyword, e.g. 'salmonella', 'aflatoxin'; year: e.g. 2025. Returns summary excerpts traceable via topic_id (full detail via get_topic_context).
| Name | Required | Description | Default |
|---|---|---|---|
| year | No | ||
| brand | No | ||
| limit | No | ||
| reason | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden. It discloses the data scope (FDA, EU RASFF, CN enforcement) and return behavior (summary excerpts traceable via topic_id), adding meaningful context. It could mention read-only semantics, but the search verb implies a safe read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two focused sentences plus parameter guidance, front-loaded with the primary purpose. No redundant text or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The output schema exists and the description explains output traceability, making it complete for a search tool. Its only minor gap is the undocumented 'limit' parameter and a lack of explicit exclusion for search_pet_topics, but the overall context is sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds semantic examples for brand, reason, and year, but omits the 'limit' parameter. Given schema description coverage is 0%, the description must compensate; it partially does but leaves a gap for one parameter, which is a meaningful omission.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Search') and identifies the resource ('pet-food recall / enforcement data') with explicit data sources (FDA, EU RASFF, CN enforcement). It clearly distinguishes from sibling tools like get_topic_context, which provides full detail, and search_pet_topics, which searches topics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly indicates that full detail is available via get_topic_context, providing a definitive follow-up path. While it does not explicitly state when not to use this tool, the examples and differentiation from siblings imply the appropriate use case.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.