vault-mcp

English | 中文

A personal knowledge vault MCP server for Claude Desktop — capture, search, promote, and connect your ideas, all through natural conversation.

https://github.com/user-attachments/assets/0ef205a4-0ffc-4a24-a92a-b4acf66377fe

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English

Personal knowledge vault MCP server for Claude Desktop. Capture thoughts, search notes, and read files from a local markdown-based knowledge vault.

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Quick Start (uvx — Recommended)

The lightest way to run vault-mcp. No Docker, no manual venv — just uv and one config change.

Step 1. Install uv (if you don't have it):

curl -LsSf https://astral.sh/uv/install.sh | sh

Step 2. Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "vault": {
      "command": "uvx",
      "args": ["obsidian-in-a-vat-mcp"],
      "env": {
        "VAULT_LOCAL_PATH": "/Users/yourname/my-vault"
      }
    }
  }
}

Replace /Users/yourname/my-vault with the absolute path to your local vault directory.

Step 3. Fully quit and reopen Claude Desktop. The vault tools will appear automatically.

Don't have a vault yet? Just point VAULT_LOCAL_PATH to an empty directory. On first use, ask Claude to "initialize my vault" — it will set up the full directory structure automatically.

Already have an Obsidian vault? Point VAULT_LOCAL_PATH to your existing vault and ask Claude to "initialize my vault". It will scan your notes, classify them (captures vs. notes), and migrate everything into the vault-mcp format. Originals are safely archived under _archive/.

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Alternative Setup (Docker)

If you prefer Docker:

{
  "mcpServers": {
    "vault": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "-v", "/Users/yourname/my-vault:/vault",
        "ghcr.io/oliverxuzy-ai/obsidian-in-a-vat:latest"
      ]
    }
  }
}

Requires Docker Desktop running in the background.

Update to latest: docker pull ghcr.io/oliverxuzy-ai/obsidian-in-a-vat:latest

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Local Development

To run from a local checkout (changes take effect after restarting Claude Desktop):

{
  "mcpServers": {
    "vault": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/absolute/path/to/obsidian-in-a-vat",
        "vault-mcp"
      ],
      "env": {
        "VAULT_LOCAL_PATH": "/Users/yourname/my-vault"
      }
    }
  }
}

Switch back to the published version by changing command to "uvx" and args to ["obsidian-in-a-vat-mcp"].

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Tools

Tool	Actions	Description
vault_init	setup, migrate	One-click vault initialization: seed empty vaults from template, or migrate existing Obsidian notes with server-side classification, todo conversion, and auto-archiving
vault_read	search, get, list_captures	Search vault, read files, list captures by status
vault_capture	save, delete	Capture refined insights with auto-tagging, or delete captures
vault_promote	promote	Promote captures into structured notes with auto-wikilinks
vault_analyze	rebuild_graph, clusters, connections, orphans	Knowledge graph: build graph, Louvain clustering, N-degree connections, orphan detection
vault_reflect	snapshot, drift, blindspots	Cognitive visualization: knowledge landscape snapshot, interest drift over time, blind spot and bridge detection
vault_topic	prepare, create, update	Topic lifecycle: gather materials (progressive disclosure), create/update MOC-style topics

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Auto-Tag Extraction

Tags are extracted from capture text using three sources (in priority order):

1. tags.yaml — Custom tags and synonym mappings at the vault root
2. Existing notes — Tags collected from existing vault files' frontmatter
3. Default domains — Fallback: ai, llm, productivity, writing, coding, design, business, learning, health, finance, philosophy, psychology

Example tags.yaml in your vault root:

tags:
  ai: [artificial intelligence, machine learning, ML, deep learning]
  coding: [programming, software, development, code]
  design: [UX, UI, user experience]

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Development

# Build image locally
docker build -t vault-mcp .

# Test the container starts (Ctrl+C to stop)
echo '{}' | docker run -i --rm -v $(pwd)/example_vault:/vault vault-mcp

# Syntax check
python -m py_compile src/vault_mcp/server.py

# Interactive MCP Inspector
mcp dev src/vault_mcp/server.py

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中文

个人知识库 MCP 服务器，适配 Claude Desktop。捕获想法、搜索笔记、读取本地 markdown 知识库中的文件。

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快速开始（uvx — 推荐）

最轻量的运行方式。不需要 Docker，不需要手动创建虚拟环境 — 只需安装 uv 即可。

第一步. 安装 uv（如果还没有）：

curl -LsSf https://astral.sh/uv/install.sh | sh

第二步. 添加到 Claude Desktop 配置文件（~/Library/Application Support/Claude/claude_desktop_config.json）：

{
  "mcpServers": {
    "vault": {
      "command": "uvx",
      "args": ["obsidian-in-a-vat-mcp"],
      "env": {
        "VAULT_LOCAL_PATH": "/Users/yourname/my-vault"
      }
    }
  }
}

将 /Users/yourname/my-vault 替换为你本地 vault 目录的绝对路径。

第三步. 完全退出并重新打开 Claude Desktop，vault 工具会自动出现。

还没有 vault？ 将 VAULT_LOCAL_PATH 指向一个空目录即可。首次使用时让 Claude "初始化我的 vault" — 它会自动创建完整的目录结构。

已有 Obsidian vault？ 将 VAULT_LOCAL_PATH 指向你现有的 vault 目录，让 Claude "初始化我的 vault"。它会扫描你的笔记，自动分类（capture vs. note），并批量迁移为 vault-mcp 格式。原始文件安全归档到 _archive/。

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备选安装方式（Docker）

如果你更喜欢用 Docker：

{
  "mcpServers": {
    "vault": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "-v", "/Users/yourname/my-vault:/vault",
        "ghcr.io/oliverxuzy-ai/obsidian-in-a-vat:latest"
      ]
    }
  }
}

需要 Docker Desktop 在后台运行。

更新到最新版：docker pull ghcr.io/oliverxuzy-ai/obsidian-in-a-vat:latest

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本地开发

从本地代码运行（修改代码后重启 Claude Desktop 即可生效）：

{
  "mcpServers": {
    "vault": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/绝对路径/obsidian-in-a-vat",
        "vault-mcp"
      ],
      "env": {
        "VAULT_LOCAL_PATH": "/Users/yourname/my-vault"
      }
    }
  }
}

切回已发布版本：将 command 改为 "uvx"，args 改为 ["obsidian-in-a-vat-mcp"]。

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工具

工具	Actions	说明
vault_init	setup, migrate	一键初始化：空 vault 自动创建模板结构；已有 Obsidian vault 自动扫描分类、todo 转换、批量迁移，原始文件归档到 _archive/
vault_read	search, get, list_captures	搜索 vault、读取文件、按状态列出 captures
vault_capture	save, delete	捕获精炼洞察并自动打标签，或删除 capture
vault_promote	promote	将 captures 提升为结构化笔记，自动插入 wikilinks
vault_analyze	rebuild_graph, clusters, connections, orphans	知识图谱：构建图谱、Louvain 聚类、N 度关联查询、孤岛检测
vault_reflect	snapshot, drift, blindspots	认知可视化：知识全景快照、兴趣漂移分析、盲区与桥接发现
vault_topic	prepare, create, update	Topic 生命周期：收集原材料（渐进式披露）、创建/更新 MOC 结构笔记

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自动标签提取

标签从 capture 文本中提取，使用三个来源（按优先级排序）：

1. tags.yaml — vault 根目录的自定义标签和同义词映射
2. 已有笔记 — 收集已有 vault 文件 frontmatter 中的标签进行匹配
3. 默认领域 — 兜底列表：ai, llm, productivity, writing, coding, design, business, learning, health, finance, philosophy, psychology

tags.yaml 示例（放在 vault 根目录）：

tags:
  ai: [artificial intelligence, machine learning, ML, deep learning]
  coding: [programming, software, development, code]
  design: [UX, UI, user experience]

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开发

# 本地构建镜像
docker build -t vault-mcp .

# 测试容器启动（Ctrl+C 停止）
echo '{}' | docker run -i --rm -v $(pwd)/example_vault:/vault vault-mcp

# 语法检查
python -m py_compile src/vault_mcp/server.py

# 使用 MCP Inspector 交互测试
mcp dev src/vault_mcp/server.py