WorkingMemory

Persistent personal memory for AI assistants — save, search, and recall across every MCP client.

사용해야 할까요

품질 및 안전성

A
설명 품질
95%
스키마 완전성
100%
이름 품질
95%
오염 위험
80%
권한 일치
100%
프로토콜 준수
100%

발견 사항 (3)

  • HIGHTool poisoning patterns detected
  • MEDIUMTool 'save_memory' description contains placeholder textsave_memory에서
  • INFOTool description contains placeholder or incomplete textsave_memory에서

도구 정의와 프로토콜 준수에 대한 자동 분석을 기반으로 합니다.

컨텍스트 비용

~908토큰 (도구 정의)
~2.0 KB일반적인 응답 크기
중간 정도의 주의 영향 (128k 컨텍스트의 0.71%)

이는 서버의 도구가 모델의 컨텍스트에 로드될 때마다 소비되는 대략적인 토큰 수입니다. 수치가 높을수록 다른 작업에 사용할 수 있는 주의가 줄어듭니다.

설치

원클릭 설치

`claude_desktop_config.json` 파일에 다음을 추가하세요:

{
  "mcpServers": {
    "memory": {
      "url": "https://app.workingmemory.ai/mcp"
    }
  }
}

원격 엔드포인트

https://app.workingmemory.ai/mcpstreamable-http

할 수 있는 일

도구 목록

도구 (4)

🟢 읽기 전용🟡 쓰기🔴 삭제⚪ 알 수 없음
🟡save_memory(text, client)

Save a thought, insight, fact, or todo to the user's Working Memory. The text is parsed and stored as one or more structured memory items, searchable a moment later via search_memory. Also use this when the user pastes memories or notes brought from another assistant — pass the full pasted text; it is split into individual memories automatically.

입력 스키마

{
  "type": "object",
  "properties": {
    "text": {
      "type": "string",
      "description": "The thought or note to save."
    },
    "client": {
      "type": "object",
      "description": "Optional context about the originating client session. Helps users later find saves from a specific conversation. `conversation_id` groups multiple saves; `conversation_title` is human-readable.",
      "properties": {
        "conversation_id": {
          "type": "string",
          "description": "Opaque identifier; typically the client's conversation thread ID."
        },
        "conversation_title": {
          "type": "string",
          "description": "Human-readable conversation title (max 200 chars; longer values truncated server-side)."
        }
      },
      "additionalProperties": false
    }
  },
  "required": [
    "text"
  ]
}
🟢search_memory(query, k, conversation_id, dedupe_recent, client)

Search the user's Working Memory for relevant past notes and facts. Use this when the user references something they may have stored earlier, or to find related context before answering. Always include your conversation id in client.conversation_id.

입력 스키마

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "Semantic query."
    },
    "k": {
      "type": "number",
      "description": "Max results (1–20). Defaults to a sensible value."
    },
    "conversation_id": {
      "type": "string",
      "description": "Optional. Restrict results to notes stamped with this conversation_id at save_memory time. Empty / whitespace-only values are treated as absent."
    },
    "dedupe_recent": {
      "type": "boolean",
      "description": "Optional. When false, semantic matches that are also recent are still returned in `relevant` (raw semantic results rather than RAG-deduped). Defaults to true."
    },
    "client": {
      "type": "object",
      "description": "Context about the CURRENT client session. Pass your conversation/thread id as `conversation_id` — it identifies this session for cross-session memory instrumentation and does NOT filter results (use the top-level `conversation_id` parameter for filtering).",
      "properties": {
        "conversation_id": {
          "type": "string",
          "description": "Opaque identifier; typically the client's conversation thread ID."
        },
        "conversation_title": {
          "type": "string",
          "description": "Human-readable conversation title (max 200 chars; longer values truncated server-side)."
        }
      },
      "additionalProperties": false
    }
  },
  "required": [
    "query"
  ]
}
🔴manage_memory(action, id)

Manage an existing memory item. Currently supports deleting a memory by id (soft delete — recoverable for 30 days). Use search_memory to find the id first.

입력 스키마

{
  "type": "object",
  "properties": {
    "action": {
      "type": "string",
      "enum": [
        "delete"
      ],
      "description": "The management action to perform."
    },
    "id": {
      "type": "string",
      "description": "The memory item id to act on."
    }
  },
  "required": [
    "action",
    "id"
  ]
}
⚪capture_memory(task_content, task_description, client)

Capture durable facts from a task you just completed (drafting an email, summarizing a document, making a decision), as a byproduct — the user does nothing. Pass the task output/content; durable facts are extracted and stored automatically. Always include your conversation id in client.conversation_id. Returns immediately; extraction happens in the background.

입력 스키마

{
  "type": "object",
  "properties": {
    "task_content": {
      "type": "string",
      "description": "The task output/content to mine for durable facts (max 32KB)."
    },
    "task_description": {
      "type": "string",
      "description": "Optional one-line description of the task, as extraction framing."
    },
    "client": {
      "type": "object",
      "description": "Current client session context. Pass your conversation/thread id as conversation_id.",
      "properties": {
        "conversation_id": {
          "type": "string",
          "description": "Opaque conversation/thread id."
        },
        "conversation_title": {
          "type": "string",
          "description": "Human-readable title (max 200 chars)."
        }
      },
      "additionalProperties": false
    }
  },
  "required": [
    "task_content"
  ]
}

권장 프롬프트

search_research
Search for information about [topic] using WorkingMemory
예상 도구: search_memory
find_specific
Find [specific item] using WorkingMemory
예상 도구: search_memory

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