UsedBikeCenter ZAMA バイク・原付の買取査定 (Japan motorcycle buyback)

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already mark the tool as readOnly and non-destructive. The description adds that it provides estimates only and does not submit purchase applications, which clarifies its limited scope.

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 at 4 sentences, front-loaded with the main purpose. Every sentence adds value: purpose, usage triggers, sibling distinction, and prerequisite step.

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?

The description mentions return value as approximate price in yen but does not specify the exact format or the role of the 'condition' parameter (which is not in schema). No output schema exists, so more detail on the return structure could be helpful.

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 parameter details are already present. The description adds that 'model' must be the exact formal notation from search_models and provides example values for 'maker' (ホンダ／ヤマハ), adding practical usage guidance.

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 returns an approximate market price in yen for used motorcycles (バイク) based on model and condition. It uses specific verb '返す' and resource '概算相場', and distinguishes from siblings search_models and submit_kaitori_request by mentioning when to use each.

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 explicitly lists user queries that trigger this tool (e.g., 'いくらになる' / '相場は'), and states that actual purchase requests should use submit_kaitori_request. It also instructs to use search_models first if the model is not identified.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses critical behavioral details beyond the annotations: the confirmation requirement (present details, get explicit consent before setting confirm=true), the preview behavior when confirm=false, and the restriction to using only the user's own name/phone number. These are significant operational traits that the annotations (readOnlyHint=false, destructiveHint=false) do not convey. No contradiction with 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 dense but not verbose. Each sentence earns its place: intent triggers, alternative tool, optional fields, the critical confirmation flow, and the privacy constraint. It is well-structured and front-loaded with the core purpose.

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 an 11-parameter mutation tool with no output schema, the description covers the essential context: when to use, what data to collect, the confirm/preview behavior, and the privacy restriction. It lacks an explicit statement of what happens after a successful submission (e.g., the request is registered), which is a minor gap, but overall it is largely complete for correct invocation.

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%, providing descriptions for all 11 parameters, so the baseline is 3. The description adds context not in the schema, such as the confirm flag semantics (preview vs. send) and the constraint that only the user's own info is allowed. It reinforces preferred_1–3 optionality, adding modest value 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 the tool's purpose: submitting a free home-pickup purchase request for motorcycles/mopeds/scooters when the user expresses intent to sell or apply. It provides specific trigger phrases and explicitly distinguishes it from get_price, making the purpose unambiguous and well-differentiated from siblings.

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?

Explicit usage guidance is provided: use when the user shows intent to actually apply, and do not use when they merely want market prices—instead, use get_price. The description also gives concrete examples of user expressions that warrant invoking this tool, offering clear decision rules for the agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.