List model years with NHTSA recall (r) or complaint (c) data to identify available records before running detailed queries.
List model years that have recalls or complaints in the NHTSA database. Use issue_type='r' for recalls (1949–present), 'c' for complaints. Useful for discovering available data before querying.
| Name | Required | Description | Default |
|---|---|---|---|
| issue_type | Yes | 'r' for recalls, 'c' for complaints |
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, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds behavioral context by specifying the date range for recalls (1949–present) and framing the tool as a discovery step. It doesn't contradict annotations and adds useful details beyond them.
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?
Three sentences with no wasted words: the purpose is front-loaded, usage instructions follow, and a practical note about discovery is included. The structure is logical and efficient.
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 tool with one parameter, no output schema, and annotations covering safety, the description is complete. It explains what it returns (model years), how to use it (issue_type), and when to use it (before querying). Nothing essential is missing.
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% since the issue_type parameter already has a description ('r' for recalls, 'c' for complaints). The description adds extra semantic value by specifying the date range for recalls (1949–present), which is not in the schema. This goes beyond the baseline of 3 for fully covered parameters.
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 'List model years that have recalls or complaints in the NHTSA database.' This is specific (verb: list, resource: model years, scope: recalls/complaints) and distinct from sibling tools like nhtsa_makes and nhtsa_models. It also adds the purpose of discovery before querying, making the intent unmistakable.
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 explicitly instructs how to use the parameter (issue_type='r' for recalls, 'c' for complaints) and states it is 'Useful for discovering available data before querying.' This implies using it as a preliminary step before other NHTSA queries, but it does not name specific alternatives or exclusions. Still, the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.