二行程經典車資料庫

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

No annotations are provided, so the description carries the full burden. It does not state whether this is a read-only lookup, whether matching is exact/fuzzy/partial, or how many results come back — significant gaps for a search-style tool.

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?

A single front-loaded sentence that defines the operation, the input forms, and an illustrative example with zero 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 one-param lookup with no output schema or annotations, the core intent is conveyed, but match semantics, result format, and read-only nature are left unstated.

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 0% and the single 'query' param is undocumented in the schema, but the description compensates by stating the query accepts either a part model number or a name, reinforced with a concrete example (BR9ES).

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?

States a specific verb (reverse-lookup) and resource (applicable two-stroke vehicle models) keyed on a part number/name. It is clearly distinguishable from siblings like search_models and get_model_specs, though it does not name them explicitly.

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 example ('spark plug BR9ES') implies when the tool applies, but there is no explicit when-to-use, when-not-to-use, or routing to alternatives such as search_models for forward lookups.

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?

With no annotations, the description carries the full burden. It usefully discloses the kind of content returned (mixture ratio, gear oil, spark plug gap, carburetor settings), which signals a read-only lookup, but it says nothing about permissions, failure behavior when the slug is unknown, or the return structure.

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?

A single compact sentence with the resource stated up front and the returned data parenthetically listed. No wasted words, though the parenthetical could arguably be trimmed.

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 single-parameter read tool with no annotations and no output schema, the description covers what data comes back but omits slug format, error conditions, and any sibling routing. Adequate but with clear gaps for an agent to call it reliably.

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 0% and the single parameter 'slug' is undocumented in the schema, so the description must compensate. It implies the argument identifies a two-stroke scooter model ('某個二行程車款'), giving marginal meaning, but no format or accepted-values detail is provided.

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?

Names a specific verb (取得/get) and resource (保養資料/maintenance data) for a two-stroke model, and enumerates the returned fields (混合比、齒輪油、火星塞間隙、化油器設定). An agent can tell it retrieves maintenance specs, though it does not explicitly contrast itself with the sibling lookup tools like get_model_specs.

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?

There is no guidance on when to choose this over the sibling tools find_part, get_model_specs, or search_models, and no prerequisites or exclusions are stated. The agent must infer that this is the maintenance-specific lookup rather than the general specs lookup.

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?

No annotations are provided, so the description carries the disclosure burden. The verb 取得 implies a read-only lookup with no side effects, and the description usefully discloses that the return includes sourced specs plus parts/maintenance/price data. However, it says nothing about caching, auth/permission needs, or error behavior for missing slugs.

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?

A single front-loaded sentence that names the verb and core resource first, then qualifies the returned content. Efficient, with no filler, though cramming four distinct data categories into one clause slightly blurs focus.

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?

With no output schema, the description does explain the returned data categories, which is the right move. But it leaves the sibling overlap (parts/maintenance) unresolved and, lacking any annotations, does not cover the read-only/side-effect profile explicitly.

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?

With a single parameter at 100% schema description coverage, the schema already documents slug and its provenance (obtainable via search_models). The description adds no syntax or format detail beyond that, so the baseline of 3 applies.

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?

States a specific verb (取得) plus resource (二行程經典車款的規格) and even enumerates the returned content: Wikipedia-sourced specs, parts cross-reference, maintenance data, and sale prices. This makes it distinguishable in principle, though the content overlaps heavily with siblings get_maintenance and find_part, so the boundary is not fully sharp.

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 gives no when-to-use or when-not-to-use guidance and never names the alternatives (find_part, get_maintenance are also needed for parts/maintenance data). The only workflow hint — that slug comes from search_models — lives in the schema field text, not the description.

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?

With no annotations provided, the description carries the full behavioral burden. It does disclose the output kind (model codes) and the domain scope (two-stroke classics, four brands), but says nothing about matching behavior (exact vs. fuzzy), result limits, pagination, case sensitivity, or whether it is an unbounded read.

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?

A single compact sentence that front-loads the action and scope and closes with the return value. Nothing is redundant or padded.

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 one-parameter search tool with no output schema, the description covers the essentials: what is searched, the domain restrictions, and what comes back. It does not state how many results are returned or how results are shaped, which is a small residual gap rather than a blocking one.

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 the single 'query' parameter is already fully documented with examples (NSR250, RZ350). The description adds domain framing (Honda/Yamaha/Suzuki/Kawasaki two-stroke classics) that narrows what a valid query looks like, but no syntax, format, or matching details beyond the schema. Baseline 3 applies.

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 verb (搜尋/search) and a precisely bounded resource (classic two-stroke motorcycle models from Honda, Yamaha, Suzuki, Kawasaki, etc.), plus the return type (model codes). An agent can tell it apart from get_model_specs because this one retrieves identifiers rather than specifications. It stops short of explicitly naming the sibling tools it complements.

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 rather than stated: you search a model name to obtain a model code, which pairs naturally with get_model_specs/find_part that take codes. There is no explicit when-to-use vs. when-not, no mention of alternatives, and no statement about what to do when the query returns nothing.

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