用自然语言描述需求（如"人形机器人髋部高扭矩电机"）做语义召回，返回最相近的零部件。语义索引由离线哈希 TF-IDF 向量（构建时预计算、零外发、零外部模型）在当前全量实体上生成，是兼容性判定之外的"发现"通道：当你不确定零件的确切型号或参数名时，用它比关键词子串匹配更稳。索引不可用时自动降级为关键词检索，并明确告知。
| Name | Required | Description | Default |
|---|---|---|---|
| k | No | 语义召回返回的候选零部件条数（top-k），默认 5，取值 1–30；数值越大召回越广但越可能偏离查询意图。 | |
| query | Yes | 自然语言查询，如"六维力传感器 防水"或"ROS2 通信模组"。中英文均可，建议具体。 |
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 discloses that the semantic index is built offline with precomputed TF-IDF vectors, zero external calls, and zero external models, and that it falls back to keyword search with explicit notification. This is good transparency, though it doesn't detail return format or error handling.
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 front-loaded, starting with the core purpose and example, then explaining the underlying mechanism and usage guidance. Every sentence adds value without 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?
Given the tool's moderate complexity, the description covers purpose, mechanism, usage guidance, and fallback behavior. It lacks explicit return format details, but since there is no output schema, the description could have mentioned what the response contains (e.g., component IDs, scores). Still, it's fairly complete 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 description coverage is 100%, so the schema already documents both parameters well. The description adds context about the k parameter's trade-off (larger k means broader recall but more deviation from intent) and provides example queries for the query parameter, adding 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 performs semantic recall using natural language queries to return the most similar components. It distinguishes itself from sibling tools like search_components (keyword substring matching) and check_compatibility (compatibility judgment), positioning itself as a 'discovery' channel.
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 this tool: when uncertain about exact part numbers or parameter names, semantic search is more robust than keyword substring matching. It also mentions fallback behavior to keyword search when the index is unavailable, providing clear usage context.
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.
多个工具处理重叠的任务：check_compatibility、review_compatibility 和 bom_compatibility_check 都涉及兼容性验证，其中前两个尤其相似，第二个使用了与第一个相同的引擎，可能是多余的。此外，search_components 和 semantic_search 重叠但具有不同机制。虽然这些描述有所澄清，但代理仍可能选择错误工具（例如，当简单检查就足够时使用了 review_compatibility）。
大多数名称遵循 verb_noun 模式（get_component_detail、search_components、review_compatibility），但有几个偏差：semantic_search 是 adjective_verb 而非 search_semantic，而 bom_compatibility_check 将操作放在最后，与 check_bom_compatibility 相反。模式的不一致性削弱了可预测性。
使用 9 个工具，组件覆盖了搜索、详情、参数语义、标准审计、推荐以及兼容性检查的多个变体——表面上足够广，但没有膨胀。每个工具针对不同的检索或分析需求，使其合理且可管理。
该表面涵盖了该领域的大部分关键功能：搜索、详情、语义标准化、标准审计、推荐以及通过三种模式进行的兼容性检查。缺失 CRUD 操作可能表明它是一个只读库，但没有任何接口来创建或更新部件，这限制了对数据库的管理。总体而言，对于查询和验证，覆盖范围相当完整，仅有边缘缺口。