Search active consumer product recalls (CPSC, FDA, NHTSA and more) by keyword. Returns matching recalls with agency, hazard, affected products, and remedy. Use when an agent needs to know if a product, brand, baby/food/vehicle item has been recalled before recommending or buying it.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Keyword to search recalls, e.g. 'stroller', 'peloton', 'infant formula'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses that recalls come from agencies like CPSC, FDA, NHTSA and returns specific fields. However, it lacks details on limitations, pagination, or performance characteristics, making it adequate but not exceptional.
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 sentences pack essential information: what the tool does and when to use it. No unnecessary words, front-loaded with the verb and resource. Excellent conciseness.
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?
Given one required parameter, no output schema, and no annotations, the description covers the core purpose, use case, and return fields. It is complete enough for an agent to decide when to invoke it, though it could mention result count or format. Score 4.
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 coverage is 100% (one parameter with description). The description adds example keywords ('stroller', 'peloton', 'infant formula') which provide practical guidance beyond the schema's generic description. This adds marginal value, so score 3 (baseline for high coverage).
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 searches active consumer product recalls by keyword, listing specific agencies and return fields. It distinguishes from siblings like check_in_stock and check_scalper_price by specifying recall-related functionality.
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 explicitly states when to use: 'when an agent needs to know if a product... has been recalled before recommending or buying it.' It does not mention alternatives or when not to use, but the context of sibling tools makes the use case clear, earning a 4.
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 distinct purpose: availability, safety recalls, price fairness, legal claims, and card grading. No overlap or ambiguity.
Three tools use 'check_' prefix, but 'find_settlements' and 'grade_card_centering' break the pattern. Mixed verb styles reduce predictability.
5 tools is well-scoped for a consumer intelligence server, covering diverse needs without bloat.
Notably, 'grade_card_centering' is non-functional on the server (client-side only), and there are gaps like price history or general product info.