Look up active US product recalls for a product or brand against live government feeds (CPSC/FDA/NHTSA-style aggregate). Returns matching recalls with the official agency source link and last-updated date. Use to check if something is recalled before buying/using it.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Product name or brand, e.g. 'Peloton bike' or 'stroller'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It states returns include agency source link and date, and that it uses live feeds. Does not mention rate limits, authentication, or error handling, but provides core behavior.
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?
Two efficient sentences, front-loaded with purpose and outcome. No unnecessary words.
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?
For a simple tool with one parameter and no output schema, the description covers purpose, scope (US, active, government feeds), and return content. Lacks mention of edge cases like no results, but sufficient for most uses.
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?
Schema description coverage is 100% for the single parameter, and the description adds value with examples ('Peloton bike' or 'stroller') and clarifies it's for product or brand.
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 clearly states it looks up active US product recalls from government feeds, specifying verb and resource. It does not explicitly distinguish from sibling tools (counterfeit_risk, safe_check) but the domain is clear.
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?
Explicit usage context given: 'Use to check if something is recalled before buying/using it.' No exclusions or alternatives mentioned, but the use case is clear.
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 has a clearly distinct purpose: counterfeit_risk for counterfeit risk assessment, recall_lookup for product recall checks, and safe_check runs both together. There is no ambiguity or overlap in their functionality.
All tool names follow a consistent snake_case pattern with descriptive verb-noun combinations (counterfeit_risk, recall_lookup, safe_check). The naming is clear and predictable.
With 3 tools, the server is well-scoped for its domain of product safety checking. Each tool earns its place, covering counterfeit risk, recall lookup, and a combined operation without being too few or excessive.
The tool surface covers the core safety checks: counterfeit risk and recall lookup, plus a combined call for convenience. There are no obvious gaps for the stated purpose of a product safety screen.