vault-mcp

English | 中文

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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 (Docker — Recommended)

Step 1. Make sure Docker Desktop is running.

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

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

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? Copy the included template:

cp -r example_vault /Users/yourname/my-vault

Updating to the Latest Version

When a new version is released, pull the latest image before restarting Claude Desktop:

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

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Alternative Setup (Python / uv)

If you prefer not to use Docker:

# Install
uv venv && source .venv/bin/activate
uv pip install -e .

Claude Desktop config:

{
  "mcpServers": {
    "vault": {
      "command": "/absolute/path/to/.venv/bin/python",
      "args": ["-m", "vault_mcp"],
      "env": {
        "VAULT_LOCAL_PATH": "/Users/yourname/my-vault"
      }
    }
  }
}

Use the absolute path to the venv's python — Claude Desktop does not inherit your shell PATH.

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Tools

Tool	Description
vault_capture	Capture a refined insight — Claude distills your thought, confirms with you, then stores it with auto-tagging
vault_promote	Promote one or more captures into a structured note with auto-wikilink insertion
vault_search	Search the vault by text query, subdirectory, or frontmatter tags
vault_read	Read the full markdown content of a vault note
vault_list_captures	List captures filtered by status (capture / promoted / all), sorted newest first

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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（Docker — 推荐）

第一步. 确保 Docker Desktop 正在运行。

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

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

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？ 复制内置模板：

cp -r example_vault /Users/yourname/my-vault

更新到最新版本

当有新版本发布时，在重启 Claude Desktop 前先拉取最新镜像：

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

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备选安装方式（Python / uv）

如果不想使用 Docker：

# 安装
uv venv && source .venv/bin/activate
uv pip install -e .

Claude Desktop 配置：

{
  "mcpServers": {
    "vault": {
      "command": "/绝对路径/.venv/bin/python",
      "args": ["-m", "vault_mcp"],
      "env": {
        "VAULT_LOCAL_PATH": "/Users/yourname/my-vault"
      }
    }
  }
}

必须使用 venv 内 python 的绝对路径 — Claude Desktop 不继承你的 shell PATH。

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工具

工具	说明
vault_capture	捕获精炼洞察 — Claude 提炼你的想法，经确认后存储并自动打标签
vault_promote	将一个或多个 capture 提升为结构化笔记，自动插入 wikilinks
vault_search	按文本、子目录或 frontmatter 标签搜索 vault
vault_read	读取 vault 笔记的完整 markdown 内容
vault_list_captures	按状态筛选 captures（capture / promoted / all），按时间倒序排列

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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