Retrieve relevant entries from a user knowledge base using semantic search for a given query.
语义搜索用户知识库。
基于向量相似度检索最相关的用户知识条目。
Args:
query: 搜索查询文本
top_k: 返回结果数量，默认 5
Returns:
{query, results: [{id, title, snippet, tags, score, source_type, created_at}, ...]}
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| top_k | No |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It usefully explains the underlying mechanism (vector similarity) and provides a detailed return structure including fields like score and source_type. It does not mention side effects, permissions, or rate limits, but for a search operation the disclosed behavior is largely sufficient.
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 front-loaded with the core purpose in two sentences, followed by clear Args and Returns sections. Every part earns its place, and the return structure is valuable given there is no output schema.
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 simple two-parameter search tool with no annotations and no output schema, the description covers purpose, parameters, and return format adequately. The main missing piece is usage guidance relative to sibling tools, but that is partially addressed by the stated semantic-search behavior.
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 0%, so the description must compensate. It explains both parameters: query as the search query text and top_k as the number of results to return (default 5). This adds meaning beyond the bare schema types, though additional constraints on top_k are not 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?
The description states a specific verb and resource: semantic search of the user knowledge base, and explains the retrieval method (vector similarity) to return the most relevant entries. This distinguishes it from sibling tools like user_knowledge_list (which lists rather than searches semantically) and user_knowledge_add/update/delete.
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?
No explicit when-to-use or when-not-to-use guidance is given, and no alternatives are named. It does not compare itself to sibling tools such as user_knowledge_list or rag_search_knowledge, so the agent must infer the context from the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.