ユーザーが挙げたメーカー名・車名・型式・排気量から、買取査定に対応する正式なバイク車種名を検索・特定する。「CB400」「125のスクーター」「ジャイロ」「4ミニ」「原付」のような曖昧・部分的な入力を正規の車種表記に解決するために使う。相場や金額は返さない（相場は get_price を使う）。get_price を呼ぶ前に、必ずこれで正確な車種名を確定すること。
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | 車種名・メーカー名・型式・排気量の一部（日本語/英数字可。例:「ジャイロ」「PCX」「ホンダ」「JBK-JF56」） |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds valuable behavioral context by explicitly stating that it does not return prices or amounts. This clarifies the tool's output scope beyond what annotations provide, though it does not elaborate on other behaviors like pagination 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?
The description is concise, with two front-loaded sentences covering purpose, usage context, sibling distinction, and workflow. Every sentence earns its place without redundancy, making it highly 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?
Given the simplicity of the tool (one parameter, no output schema, good annotations), the description is remarkably complete. It covers what the tool does, when to use it, how it relates to get_price, and what it returns/excludes, leaving no significant gaps for the agent.
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?
The schema provides a 100% description for the query parameter, so the baseline is 3. The description adds further meaning by giving concrete examples of ambiguous inputs (e.g., 'CB400', '125のスクーター', 'ジャイロ') and explaining the resolution goal, which helps the agent understand what kinds of queries are appropriate.
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's function: searching and identifying official motorcycle model names from partial or ambiguous user input. It explicitly differentiates itself from the sibling tool get_price by stating it does not return market prices, making the purpose 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 provides explicit usage guidance: use this tool to resolve ambiguous model names before calling get_price, and use get_price for market prices instead. This clearly delineates when to use this tool versus its sibling alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool serves a distinct, non-overlapping function: search_models resolves model names, get_price provides price estimates, and submit_kaitori_request handles actual buyout requests. The descriptions explicitly cross-reference each other to prevent misuse.
All tool names follow a consistent verb_noun pattern (search_models, get_price, submit_kaitori_request). The use of 'kaitori' is domain-appropriate and does not break the naming convention.
Three tools is well-scoped for the narrow purpose of motorcycle buyback estimation and request submission. Each tool earns its place in the core workflow, and the count is neither too thin nor excessive.
The set covers the full user journey: model identification (search_models), price lookup (get_price), and final purchase request (submit_kaitori_request). The required confirmation step for the request is embedded, leaving no obvious dead ends or missing operations for the domain.
