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shijieweb

memos-api-mcp

by shijieweb

search_memory

Search stored user memories to retrieve relevant facts, preferences, and skills, enabling informed and personalized responses.

Instructions

每次回答前自动检索记忆;用户问"我是谁/我的画像"时同时调用 get_user_profile。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes检索查询词。
filterNo过滤条件,如 agent_id、create_time 等。
relativityNo相关度阈值 0-1。0 取消过滤。
include_skillNo是否检索 Skill。默认 false。
knowledgebase_idsNo搜全部知识库存传 ["all"];指定知识库传其 ID 数组;未提及则省略。
include_preferenceNo是否检索偏好记忆。默认 true。
skill_limit_numberNo返回的 Skill 最大条数。默认 6,最大 25。
include_tool_memoryNo是否检索工具记忆。默认 false。
memory_limit_numberNo返回的事实记忆最大条数。默认 9,最大 25。
preference_limit_numberNo返回的偏好记忆最大条数。默认 9,最大 25。
tool_memory_limit_numberNo返回的工具记忆最大条数。默认 6,最大 25。
conversation_first_messageYes用户在本会话的第一条消息,用于生成 conversation_id。
Behavior3/5

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

No annotations are provided, so the description carries the full transparency burden. It does disclose the automatic invocation behavior and the get_user_profile co-call condition, adding useful context beyond the tool name. However, it does not disclose side effects, return format, or explicitly confirm the operation is read-only, leaving meaningful behavioral gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two short, focused sentences. The main behavior is front-loaded, and the special case about get_user_profile is delivered as a single dependent clause. There is no wasted wording.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite a rich 12-parameter schema, the description lacks orchestration details such as how the returned memory should be used, what the return payload looks like, and whether the operation is safe/read-only. With no output schema and no annotations, the tool needs more behavioral context than this brief description provides.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema covers 100% of the parameters with detailed descriptions, so the baseline is 3. The description itself adds no parameter-level semantics beyond what the schema already provides, but it also doesn't need to since the schema is thorough.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses the specific verb '检索记忆' (retrieve memory) and clearly states the tool runs automatically before each answer, identifying it as a memory retrieval tool. It does not explicitly contrast with siblings like delete_memory or add_feedback, so sibling differentiation is limited.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states the tool should be invoked before every answer ('每次回答前自动检索记忆') and instructs that get_user_profile should also be called when the user asks about identity/profile. This provides clear when-to-use guidance and sibling coordination, though it stops short of stating when not to use the tool.

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

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