recall-risk
Server Details
Check a product's recall risk: score + active CPSC/FDA/NHTSA recalls for a product or brand.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.3/5 across 1 of 1 tools scored.
With only one tool, there is no possibility of confusion between tools. The single tool's purpose is clearly defined and distinct from anything else.
The single tool uses a clear verb_noun pattern ('recall_risk'), which is consistent within its own context. No other naming conventions exist to cause inconsistency.
One tool feels thin for a server intended to provide recall-risk signals. While the domain is narrow, additional tools for managing tracked products or viewing historical data would likely be beneficial. The count is borderline.
The server covers the core query operation but lacks tools for managing tracked items (e.g., add/remove products) or accessing historical trends. This leaves notable gaps that could hinder automated workflows.
Available Tools
1 toolrecall_riskAInspect
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'). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It explicitly states the data is a risk signal from public complaints and not an official recall, which is important for correct interpretation. It also lists output components. However, it does not mention auth requirements or rate limits.
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, front-loaded with the main action and resource. No unnecessary words. Every sentence adds value.
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 tool with one parameter, no output schema, and no annotations, the description is complete. It explains what is returned and provides a necessary caveat about the data source. No gaps.
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%, but the description adds value by specifying the parameter can be a product/brand/model name or tracked id, with examples, making the parameter clearer beyond the schema.
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 tool retrieves a recall-risk signal for a product/brand/model, using specific verbs and resource. No siblings to differentiate, but the purpose is distinct and unambiguous.
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 implies usage for tracked products but lacks explicit guidance on when to use this tool versus alternatives, or when not to use it. Since no siblings exist, the missing guidance is less critical but still absent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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