Obsidian-in-a-Vat
AI-native knowledge vault MCP server: capture thoughts, auto-promote to structured notes, and build a knowledge graph with Louvain clustering, all from Claude Desktop.
vault-mcp
English
Personal knowledge vault MCP server for Claude Desktop. Capture thoughts, search notes, and read files from a local markdown-based knowledge vault.
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
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.
Tools
| Tool | Actions | Description |
|---|---|---|
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_topic | prepare, create, update | Topic lifecycle: gather materials (progressive disclosure), create/update MOC-style topics |
Auto-Tag Extraction
Tags are extracted from capture text using three sources (in priority order):
- tags.yaml — Custom tags and synonym mappings at the vault root
- Existing notes — Tags collected from existing vault files' frontmatter
- 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
# 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、搜索笔记、读取本地 markdown 知识库中的文件。
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
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。
工具
| 工具 | Actions | 说明 |
|---|---|---|
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_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 |
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):
- tags.yaml — vault 根目录的自定义标签和同义词映射
- 已有笔记 — 收集已有 vault 文件 frontmatter 中的标签进行匹配
- 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
# 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
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