Get the current complaint-velocity recall-risk signal for a tracked product/brand/model. Returns a 0-100 risk score, band, complaint velocity & acceleration, and severe-hazard share. This is a RISK SIGNAL from public complaint data, NOT an official recall or a claim the product is defective.
| Name | Required | Description | Default |
|---|---|---|---|
| entity | Yes | Product/brand/model name or tracked id (e.g. 'Ford Mustang Mach-E', 'Dexcom G7', 'veh-tesla-modely-2023'). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the return values (0-100 risk score, band, complaint velocity, acceleration, severe-hazard share) and explicitly states it is not an official recall. With no annotations provided, the description appropriately carries the burden of transparency.
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 sentences totaling 33 words, front-loaded with purpose, and every sentence adds value—no wasted 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?
Given the simple parameter set (one required string), no output schema, and no sibling tools, the description is complete: it explains what the tool does, what it returns, and includes a necessary caveat.
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 single parameter 'entity' has a schema description, and the tool description provides examples (e.g., 'Ford Mustang Mach-E', 'Dexcom G7', 'veh-tesla-modely-2023'), adding meaningful context beyond the schema alone.
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 the verb 'Get' and the resource 'recall-risk signal for a tracked product/brand/model'. It distinguishes itself from an official recall by warning it is a risk signal from public complaint data, not an actual recall.
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?
No explicit when-to-use or when-not-to-use guidance is given. There are no sibling tools to differentiate from, but the description implies usage for obtaining a risk signal. No prerequisites (e.g., product must be tracked) are mentioned.
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.
With only one tool, there is no possibility of ambiguity between tools.
The single tool name 'recall_risk' follows a clear verb_noun pattern, consistent with best practices.
A server intended to provide recall risk signals would typically need multiple tools for different endpoints (e.g., list products, get historical trends). One tool is far too few for a meaningful service.
The tool surface is severely incomplete: only one operation is provided, lacking risk search, product listing, historical data, or any complementary functionality needed for a recall risk domain.