Retrieve relevant conversational memories from a natural-language query using weighted keyword, semantic, importance, recency, and graph signals. Returns top matches with type, hierarchy, and score.
按一句话去翻记忆。五路加权（关键词/语义/重要度/新鲜度/图谱），返回最相关的几条，带类型、层级和分数。
| Name | Required | Description | Default |
|---|---|---|---|
| k | No | 返回几条，默认 5。 | |
| query | Yes | 用自然语言描述要找什么。 | |
| scope | No | 只在某个会话/场景里找（可选）。 |
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 full burden. It does disclose the ranking mechanism (five-way weighted: keyword/semantic/importance/recency/graph) and what fields come back (type, level, score), which is genuinely useful. However, it says nothing about pagination, latency, scope defaults, or side effects.
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 tight sentences with the action front-loaded and the mechanism plus return shape packed in without waste. Every clause earns its place.
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?
There is no output schema, and the description compensates by naming the returned fields (type, hierarchy, score) and the ranking approach. For a 3-parameter read tool this is nearly complete, though it could say more about how 'scope' interacts with results.
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 all three parameters thoroughly (query, k, scope), making 3 the baseline. The description's '按一句话' hints that the query should be natural language, adding marginal 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 uses the specific verb 'recall/search memory' followed by the query-based mechanism, making it clear this is a retrieval operation in contrast to the sibling 'remember' (which stores). It doesn't name the siblings explicitly, so it lands at 4 rather than 5.
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 opening phrase '按一句话去翻记忆' implies usage (natural-language query retrieval), but there's no explicit when-to-use, when-not-to-use, or pointer to alternatives like 'brief' or 'stats'. The guidance is only implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.