List vehicle makes for a model year that have recalls or complaints. Specify issue_type 'r' for recalls or 'c' for complaints.
List vehicle makes for a model year that have recalls or complaints. Use issue_type='r' for recalls, 'c' for complaints.
Example: model_year=2024, issue_type='r'
| Name | Required | Description | Default |
|---|---|---|---|
| issue_type | Yes | 'r' for recalls, 'c' for complaints | |
| model_year | Yes | Model year |
Changes observed during successful MCP inspections.
v2026.4.11Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety. The description adds that it returns makes associated with recalls or complaints but does not disclose pagination, ordering, or potential limitations. Given the annotations, this level is adequate but not exhaustive.
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 extremely concise: two short sentences plus an example. It leads with the core purpose and packs usage guidance efficiently without any filler.
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 list endpoint with two parameters and no output schema, the description covers the essential behavior and provides a practical example. It doesn't describe response format or limits, but those are not critical for understanding how to invoke the 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?
Schema description coverage is 100%, so both parameters are already documented. The description re-emphasizes the issue_type values and provides an example, which adds minor clarity but does not introduce new semantic depth 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 it lists vehicle makes for a model year based on issue type, using specific verb 'List'. It distinguishes itself by focusing on makes with recalls/complaints, though it does not explicitly name any sibling tool. The purpose is 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?
It provides explicit parameter usage ('Use issue_type=\'r\' for recalls, \'c\' for complaints') and a concrete example. However, it does not clarify when to choose this tool over related tools like nhtsa_models or nhtsa_recalls, so there's a slight gap in alternative routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.