mcp-markdown-vault

mcp-markdown-vault

Headless semantic MCP server for Obsidian, Logseq, Dendron, Foam, and any markdown folder. Features built-in hybrid semantic search, surgical AST editing, template scaffolding, zero-config local embeddings, and workflow tracking.

Category
访问服务器

README

<div align="center">

📁 Markdown Vault MCP Server

Headless semantic MCP server for Obsidian, Logseq, Dendron, Foam, and any folder of markdown files.

npm install and point it at a folder. Hybrid search, AST editing, zero-config embeddings. No app, no plugins, no API keys.

<!-- Note: Badge URLs reference the current GitHub repo (Wirux/mcp-obsidian). --> <!-- Update these if/when the repo is renamed to mcp-markdown-vault. --> CI / Release PR Check npm version Docker License: MIT TypeScript Node.js Tests mcp-markdown-vault MCP server

</div>

<div align="center">

Markdown Vault MCP Server Demo

</div>


💡 Why this server?

TL;DR — One npx command. No running app. No plugins. No vector DB. Semantic search works out of the box.

Differentiator Details
🚫 No app or plugins required Most Obsidian MCP servers (mcp-obsidian, obsidian-mcp-server) need Obsidian running with the Local REST API plugin. This server reads and writes .md files directly — point it at a folder and go.
🧠 Built-in semantic search, zero setup Hybrid search: cosine-similarity vectors + TF-IDF + word proximity. Local embeddings (@huggingface/transformers, all-MiniLM-L6-v2, 384d) download on first run. No API keys, no external services. Ollama optional for higher quality.
🔬 Surgical AST-based editing remark AST pipeline patches specific headings or block IDs without touching the rest of the file. Freeform line-range & string replace as fallback. Levenshtein fuzzy matching handles LLM typos.
🔓 Tool-agnostic Obsidian vaults, Logseq graphs, Dendron workspaces, Foam, or any plain folder of .md files. If it's markdown, it works.
📦 Single package, no infrastructure Unlike Python alternatives that need ChromaDB or other vector stores, everything runs in one Node.js process. npx @wirux/mcp-markdown-vault and you're running. Docker image available.

<div align="center">

💎 Obsidian · 📓 Logseq · 🌳 Dendron · 🫧 Foam · 📂 Any .md folder

</div>


✨ Features

Feature Description
🗂️ Headless vault ops Read, create, update, edit, delete .md notes with strict path traversal protection
📑 Read by heading Read a single section by heading title — returns only content under that heading (up to the next same-level heading), saving context window space
📦 Bulk read Read multiple files and/or heading-scoped sections in a single call — reduces MCP round-trips with per-item fault tolerance
🔬 Surgical editing AST-based patching targets specific headings or block IDs — never overwrites the whole file
🔍 Fragment retrieval Heading-aware chunking + TF-IDF + proximity scoring returns only relevant sections
📂 Scoped search Optional directory filter for global_search and semantic_search — restrict results to specific folders to reduce noise
🧠 Semantic search Hybrid vector + lexical search with background auto-indexing
Zero-setup embeddings Built-in local embeddings via @huggingface/transformers — Ollama optional
🔄 Workflow tracking Petri net state machine with contextual LLM hints
🌐 Dual transport Stdio (single client) or SSE over HTTP (multi-client, Docker-friendly)
✏️ Freeform editing Line-range replacement and string find/replace as AST fallback
🏷️ Frontmatter management AST-based read and update of YAML frontmatter — safely manage tags, statuses, and metadata without corrupting file structure
👀 Dry-run / diff preview Preview any edit operation as a unified diff without saving — set dryRun=true on any edit action
📝 Templating / scaffolding Create new notes from template files with {{variable}} placeholder injection — refuses to overwrite existing files
🗺️ Vault overview Structural map of the vault — total file count, recursive folder tree with file counts and last modification dates per folder
📦 Batch edit Apply multiple edit operations in a single call — sequential execution, stops on first error, supports dryRun, max 50 ops
🔗 Backlinks index Find all notes linking to a given path — supports wikilinks and markdown links with line numbers and context snippets
🎯 Typo resilience Levenshtein-based fuzzy matching for edit operations

🛠️ MCP Tools

Tool Actions Description
📁 vault list read create update delete stat create_from_template Full CRUD for vault notes + template scaffolding
✏️ edit append prepend replace line_replace string_replace frontmatter_set batch AST-based patching + freeform fallback + frontmatter update + batch edit (supports dryRun diff preview)
👁️ view search global_search semantic_search outline read frontmatter_get bulk_read backlinks Fragment retrieval, cross-vault search, hybrid semantic search, read by heading, frontmatter read, bulk read, backlinks
🔄 workflow status transition history reset Petri net state machine control
⚙️ system status reindex overview Server health, indexing info, vault structure overview

All tool responses include contextual hints based on the current workflow state.


🚀 Quick Start

Prerequisites

📦 Install from NPM

npm install -g @wirux/mcp-markdown-vault

Then run directly:

VAULT_PATH=/path/to/your/vault markdown-vault-mcp

🔌 MCP Client Configuration

Add to your MCP client config (e.g. Claude Desktop, Claude Code):

{
  "mcpServers": {
    "markdown-vault": {
      "command": "npx",
      "args": ["-y", "@wirux/mcp-markdown-vault"],
      "env": {
        "VAULT_PATH": "/path/to/your/vault"
      }
    }
  }
}

npx -y auto-installs the package if not already present — no global install needed.

Try it in the browser: You can test this server directly at Glama Inspector — no local install required.

🐳 Docker

Pull the pre-built multi-arch image from GitHub Container Registry:

docker pull ghcr.io/wirux/mcp-markdown-vault:latest

Or use Docker Compose:

docker compose up

Edit docker-compose.yml to point at your markdown vault directory. The default compose file uses SSE transport on port 3000.

🛠️ Development (from source)

git clone https://github.com/Wirux/mcp-obsidian.git
cd mcp-obsidian
npm install
npm run build
VAULT_PATH=/path/to/your/vault node dist/index.js

🌐 Transport Modes

Mode Use case How it works
📡 stdio (default) Single-client desktop apps (Claude Desktop) Reads/writes stdin/stdout; 1:1 connection
🌊 sse Multi-client setups (Docker, Claude Code) HTTP server with SSE streams; one connection per client

SSE starts an HTTP server on PORT (default 3000):

  • GET /sse — establishes an SSE stream (one per client)
  • POST /messages?sessionId=... — receives JSON-RPC messages
MCP_TRANSPORT_TYPE=sse PORT=3000 VAULT_PATH=/path/to/vault npx @wirux/mcp-markdown-vault

Each SSE client gets its own workflow state. Shared resources (vault, vector index, embedder) are reused across all connections.


🧠 Embedding Providers

The server selects an embedding provider automatically:

OLLAMA_URL set? Ollama reachable? Provider used
❌ No 🏠 Local (@huggingface/transformers, all-MiniLM-L6-v2, 384d)
✅ Yes ✅ Yes 🦙 Ollama (nomic-embed-text, 768d)
✅ Yes ❌ No 🏠 Local (fallback with warning)

No configuration needed for local embeddings — the model downloads on first use and is cached automatically.


⚙️ Configuration

Variable Default Description
VAULT_PATH /vault Markdown vault directory
MCP_TRANSPORT_TYPE stdio stdio (single client) or sse (multi-client HTTP)
PORT 3000 HTTP port (SSE mode only)
OLLAMA_URL (unset) Set to enable Ollama embeddings
OLLAMA_MODEL nomic-embed-text Ollama embedding model name
OLLAMA_DIMENSIONS 768 Ollama embedding vector dimensions

🏗️ Architecture

Clean Architecture with strict layer separation:

src/
├── domain/           🔷 Errors, interfaces (ports), value objects
├── use-cases/        🔶 Business logic (AST, chunking, search, workflow)
├── infrastructure/   🟢 Adapters (file system, Ollama, vector store)
└── presentation/     🟣 MCP tool bindings, transport layer (stdio/SSE)

See CLAUDE.md for detailed architecture docs and CHANGELOG.md for implementation history.


🚢 CI/CD & Release

Fully automated via GitHub Actions and Semantic Release:

Workflow Trigger What it does
PR Check Pull request to main Lint → Build → Test
Release Push to main Lint → Test → Semantic Release (NPM + GitHub Release) → Docker build & push to ghcr.io

🧪 Testing

318 tests across 31 files, written test-first (TDD).

npm test                                          # Run all tests
npx vitest run src/use-cases/ast-patcher.test.ts  # Single file
npm run test:watch                                # Watch mode
npm run test:coverage                             # Coverage report

Tests use real temp directories for file system operations and in-memory MCP transport for integration tests. No external services required.


🔒 Security

  • 🛡️ All file paths validated through SafePath value object before any I/O
  • 🚫 Blocks path traversal: ../, URL-encoded (%2e%2e), double-encoded (%252e), backslash, null bytes
  • ✍️ Atomic file writes (temp file + rename) prevent partial writes
  • 👤 Docker container runs as non-root user

📄 License

MIT

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选