RoboParts 机器人零部件兼容性

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

With no annotations, the description carries the full burden and adequately discloses key behavioral traits: conclusions are rule-based from manufacturer declarations, not lab-tested, and unstated dimensions are marked '无法判定' and excluded from compatibility counts. This adds important context beyond the schema.

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: one main sentence stating purpose and output, plus a caveat about the inference basis. Every sentence provides essential information, with no filler or repetition.

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 tool with simple parameters and no output schema, the description covers the core return types and the crucial caveat about undeclared dimensions. It doesn't explain edge cases like invalid IDs or exact possible per-dimension values, but it provides sufficient context for an agent to decide when to invoke it.

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% (both component IDs have descriptions), so the baseline is 3. The description adds no extra parameter-specific semantics beyond stating it involves '两个零部件', which is already evident from the parameter names.

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 identifies the tool's verb ('判定'), resource ('两个零部件'), and scope (four dimensions: protocol/electrical/mechanical/software). It also specifies the return content (per-dimension conclusion, overall judgment, confidence), distinguishing it from sibling tools like get_component_detail or search_components.

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 clear context on when to use the tool (e.g., checking compatibility across four dimensions) and sets expectations about the inference-based nature. It doesn't explicitly mention when not to use it or name alternatives, but the sibling list and the tool's specific purpose make usage appropriate clear.

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 discloses the return content (source_tier, confidence, data_quality, mechanical_interface) and the purpose, but does not cover error handling, permissions, or response format. For a read-only get operation, this is acceptable but lacks deeper behavioral context.

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?

Two sentences, no wasted words. The first sentence is action-focused (what it does), the second provides usage context. Perfectly front-loaded and concise.

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 tool is simple (1 param, no output schema, no annotations). The description explains the return fields and the decision-making context. While it doesn't exhaustively enumerate all fields, it gives enough for an agent to understand the tool's role, making it sufficiently complete for a get-by-ID operation.

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%: the single 'id' parameter is described with examples (ACT-001 / CHIP-001 / SENS-001). The description adds no additional parameter meaning beyond what the schema already provides, 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?

The description clearly states the tool retrieves a single component's full fields by ID, using a specific verb (获取/get) and resource (单个零部件/single component). It distinguishes from siblings like search_components, which presumably returns a search result list, by emphasizing 'complete fields' and metadata for a single entity.

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 second sentence provides clear context: used to verify evidence strength before procurement/design decisions. This tells the agent when to invoke it, though it does not explicitly mention alternatives or when not to use it.

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 transparency burden. It discloses important behavioral context: the database contains many torque/speed variants (19 torque calibers, 37 speed calibers, even Gbps and rad/s mixed in), and the tool returns normalization aids rather than modifying data. It does not state edge cases like failure modes, but it goes well beyond a bare 'return parameter semantics' statement.

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 two sentences, front-loaded with the core purpose, and every clause earns its place: the first sentence establishes the problem (semantic ambiguity), and the second lists the outputs and use case. There is no 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 zero-parameter metadata lookup tool, the description is complete: it explains why the tool exists, the data-quality issues it addresses, what it returns, and how to apply the result. Since there is no output schema, the detailed enumeration of return components (physical red lines, unit conversions, comparability grades, vendor inquiry list) is sufficient.

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 tool has zero parameters and the input schema is empty, so the baseline is 4. The description adds context about the domain (torque/speed calibers and unit ambiguity) that helps an agent understand what the tool operates on, even though there are no explicit parameters to document.

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 opens with a specific action ('获取参数口径规范') and clearly identifies the resource (parameter definition norms). It then lists concrete outputs (physical red lines, unit conversion, comparability grading, vendor inquiry checklist) and states the intended use ('判断两份参数表到底能不能直接比较'), which distinguishes it from sibling tools like search_components or recommend_for_application.

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 states when to use the tool: when determining whether two parameter tables can be directly compared, especially in cross-vendor contexts with ambiguous units. It does not explicitly name sibling tools or state when not to use it, but the context is clear enough for an agent to select it appropriately.

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 burden of behavioral disclosure. It transparently states that the tool performs a heuristic filter based on library fields, does not constitute engineering selection advice, and requires final verification against manufacturer datasheets. This is meaningful context beyond the raw schema, though it does not mention return format details or any rate limits/authentication.

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 and well-structured: the first sentence states the core purpose, the second describes the output, and the third provides an important caveat. Every sentence earns its place, with no redundant or filler content.

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 tool lacks an output schema, but the description does explain what is returned (candidates per category and reasons). It also adds critical context about the heuristic nature and need for datasheet verification. However, it could be more specific about how the count and budget parameters affect the output, though the schema covers their meanings. Overall, it is mostly complete for the tool's complexity.

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% - all three parameters (application, count, budget) have descriptions in the schema. The description adds only the 'USD' unit for budget, which is a minor addition. Since the schema already documents parameter semantics thoroughly, the description adds minimal value beyond that, resulting in a baseline score of 3.

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: recommending component combinations based on application scenario, with an optional budget cap. It distinguishes itself from sibling tools (search_components, check_compatibility, get_component_detail, get_parameter_semantics) by focusing on application-driven recommendations rather than searching, compatibility checking, or detail retrieval.

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 implies when to use this tool (when you have an application scenario and need a starting point for component selection) and provides a caveat that it is heuristic and not a substitute for engineering judgment. However, it does not explicitly state when not to use it or name alternatives for other use cases, so it misses the top tier for explicit exclusions.

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 burden. It discloses that results are summaries, the library contains 688 entities across 10 categories, and that filtering is available. However, it does not explicitly state that the operation is read-only or that include_market_intelligence defaults to false, though these are covered in the schema.

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 two concise sentences that front-load the purpose and filters, then provide useful scope information. Every sentence earns its place with no redundancy.

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 compensates by listing the exact return fields. It also provides context about the library size and categories. It could possibly mention the special include_market_intelligence parameter, but that is in the schema. Overall, it's a complete description for a search tool.

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 descriptions cover all 4 parameters (100% coverage), so the baseline is 3. The description adds little beyond the schema, only mentioning category and keyword filters which are already documented.

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 searches robot components with category and keyword filters, and specifies the return format (summaries with id/name/category/manufacturer/specs/evidence level). This distinguishes it from sibling tools like get_component_detail or recommend_for_application.

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 implies usage as a search tool but does not explicitly contrast it with sibling tools or state when not to use it. It does not mention that get_component_detail should be used for full details, though returning summaries hints at this.

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