jp_complaints_summaryOwner defect reports filed with MLIT's 不具合情報ホットライン (39954 across 313 models): total reports for a model and the breakdown by defective device (brakes, lights, engine, body…) from the newest sample. Reports are unverified owner submissions — compare shapes, not raw counts. Accepts English or Japanese names.
| Name | Required | Description | Default |
|---|---|---|---|
| make | Yes | e.g. honda | |
| model | Yes | e.g. n-box |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, idempotent, non-destructive, and closed-world behavior. The description adds meaningful context beyond that: the data source, corpus size (39,954 reports across 313 models), that results come from the newest sample, and an important caveat that reports are unverified owner submissions.
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 front-loaded with the core purpose and return shape, then follows with a caveat and input-language note. It is dense but appropriately sized, with no clearly wasted sentences, though the first sentence is somewhat long.
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 two-parameter summary tool with no output schema, the description explains what is returned, where the data comes from, a key reliability caveat, and the accepted input languages. It is mostly complete, though 'newest sample' could be clearer and no return structure details are given.
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%, so both required parameters are already documented with examples. The description adds useful semantics by noting that make and model accept either English or Japanese names, which is not captured in the schema examples.
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 states a specific resource—owner defect reports filed with MLIT's hotline—and specifies the returned summary: total reports for a model plus a breakdown by defective device. It distinguishes itself from recall-focused siblings implicitly through the complaints framing, though it does not explicitly name or contrast sibling tools.
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?
Usage is implied by the complaint-report domain, and the caution to 'compare shapes, not raw counts' gives interpretive guidance. However, the description does not explicitly state when to use this versus recall or reliability tools, nor does it name alternatives or exclusions.
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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