vault-mcp

> A personal knowledge vault MCP server for Claude Desktop — capture thoughts, connect ideas, and reflect on how your thinking is changing, all through natural conversation.

  ·  

Showcase

A vault is more than a folder of notes — it's a mirror of how you think. `vault_reflect` turns that mirror into something you can look at.

📺 Watch the full demo (60s)

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

Why this exists

Most note-taking tools stop at storage. `vault-mcp` is built around a three-layer vision:

• L1 · Capture — frictionless save from any conversation, with auto-tagging and slug generation.
• L2 · Connect — promote raw captures into structured notes with auto-wikilinks, build a knowledge graph, find orphans and bridges.
• L3 · Reflect — visualize your knowledge landscape, surface interest drift, and discover blind spots over time.

The goal isn't to replace Obsidian. It's to give Claude the hands and eyes to work *inside* your vault.

Workflow

flowchart LR
    Chat(["💬 Chat with Claude"]) -->|vault_capture| Cap[("📥 captures/")]
    Cap -->|vault_promote| Notes[("📝 notes/")]
    Notes -->|vault_analyze| Graph["🕸️ knowledge graph"]
    Notes -->|vault_topic| Topics[("🗺️ topics/ · MOC")]
    Cap -.->|vault_reflect| Mirror["🪞 snapshot · drift · blindspots"]
    Notes -.->|vault_reflect| Mirror
    Graph -.->|vault_reflect| Mirror

    classDef store fill:#eef2ff,stroke:#4c8bf5,color:#1e3a8a
    classDef view fill:#f0fdf4,stroke:#16a34a,color:#14532d
    classDef chat fill:#fef3c7,stroke:#d97706,color:#7c2d12
    class Cap,Notes,Topics store
    class Graph,Mirror view
    class Chat chat

Each tool plays a distinct role: `vault_capture` lands raw thoughts, `vault_promote` refines them into notes, `vault_analyze` & `vault_topic` weave them together, and `vault_reflect` lets you step back and see the whole picture.

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/`.

Alternative setup — 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`

Local development setup

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"]`.

Tools

Tools are grouped by the three layers of the vision.

📥 Capture & Read

🔗 Connect

🪞 Reflect

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]

Development

# Run all tests
uv run pytest tests/ -v

# 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

中文

个人知识库 MCP 服务器，适配 Claude Desktop —— 捕获想法、连接笔记、反思自己思维的变化，全部通过自然对话完成。

一图看懂

vault 不只是一个文件夹，而是你思维的一面镜子。`vault_reflect` 把这面镜子变成了你可以"看"的东西。

设计哲学

大多数笔记工具止步于"存储"。`vault-mcp` 围绕三层愿景设计：

• L1 · Capture — 任何对话中无摩擦地保存想法，自动打标签、生成 slug。
• L2 · Connect — 把原始 capture 提升为结构化笔记，自动插入 wikilinks，构建知识图谱，发现孤岛和桥接。
• L3 · Reflect — 可视化你的知识全景，呈现兴趣漂移，长期暴露盲区。

目标不是替代 Obsidian，而是让 Claude 拥有在你 vault 里"动手"和"看见"的能力。

工作流

flowchart LR
    Chat(["💬 与 Claude 对话"]) -->|vault_capture| Cap[("📥 captures/")]
    Cap -->|vault_promote| Notes[("📝 notes/")]
    Notes -->|vault_analyze| Graph["🕸️ 知识图谱"]
    Notes -->|vault_topic| Topics[("🗺️ topics/ · MOC")]
    Cap -.->|vault_reflect| Mirror["🪞 snapshot · drift · blindspots"]
    Notes -.->|vault_reflect| Mirror
    Graph -.->|vault_reflect| Mirror

    classDef store fill:#eef2ff,stroke:#4c8bf5,color:#1e3a8a
    classDef view fill:#f0fdf4,stroke:#16a34a,color:#14532d
    classDef chat fill:#fef3c7,stroke:#d97706,color:#7c2d12
    class Cap,Notes,Topics store
    class Graph,Mirror view
    class Chat chat

每个工具有清晰的分工：`vault_capture` 接住原始想法，`vault_promote` 把它们提炼成笔记，`vault_analyze` 和 `vault_topic` 把笔记编织起来，`vault_reflect` 让你后退一步看到全貌。

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/`。

备选安装方式 — 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`

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"]`。

工具

按三层愿景分组。

📥 Capture & Read · 捕获与读取

🔗 Connect · 连接

🪞 Reflect · 反思

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]

开发

# 运行所有测试
uv run pytest tests/ -v

# 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