Automotive Recall Data Sources
automotive_safety_source_directoryDocument access status, scope and limitations of US NHTSA recall, complaint and VIN services.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
automotive_safety_source_directoryDocument access status, scope and limitations of US NHTSA recall, complaint and VIN services.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds that the tool 'documents' information, which suggests a non-mutating, informational response. However, it does not disclose what form the documentation takes (e.g., static text, structured list) or whether it covers all NHTSA endpoints. The description adds some behavioral context but not deeply, given the strong annotations.
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 a single, well-structured sentence with no filler. It front-loads the core purpose ('Document access status, scope and limitations') and specifies the scope. Every word contributes meaning. It is as concise as possible while remaining informative.
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 directory tool intended to guide usage of NHTSA services, the description is too sparse. It does not mention what the tool returns (e.g., a list of available endpoints, authentication details, rate limits), nor does it point to related tools for actual data retrieval. With no output schema and no additional context, an agent may call this tool and be uncertain how to interpret the result or how to proceed to the actual recall services. This is a significant gap for a meta-tool.
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?
There are zero parameters, so there is nothing for the description to explain beyond the schema. The schema itself is trivially complete (an empty object). Per the calibration rule, 0 parameters warrants a baseline of 4. The description does not need to add parameter-specific context, and it does not leave any parameter ambiguity.
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 starts with a specific verb ('Document'), names the exact resource (US NHTSA recall, complaint and VIN services), and specifies what is documented (access status, scope, limitations). This clearly distinguishes it from sibling tools like nhtsa_vehicle_recalls_basic (which likely returns recall data) and aerospace_source_directory (different domain). An agent can immediately grasp what this tool is for.
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?
There is no guidance on when to use this tool versus alternatives. It does not mention that this is a meta-tool to consult before using the actual NHTSA services, nor does it reference any sibling tools. The context implies a directory/overview, but explicit 'use this when...' or 'instead of...' instructions are absent, leaving the agent to infer the use case.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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