MCP Obsidian

MCP Obsidian

Enables semantic search across Obsidian vaults using vector embeddings and ChromaDB. Supports multiple vaults with real-time indexing and provides both MCP server and CLI interfaces for natural language querying of notes.

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mcp-obsidian

An MCP (Model Context Protocol) server for semantic search in Obsidian vaults using embedded ChromaDB vector storage. I intend on keeping this fairly minimal to keep usage with Claude simple.

Features

  • 🔍 Semantic search across your Obsidian vaults using vector embeddings
  • 📅 Temporal search to find notes by modification date with optional semantic filtering
  • 📁 Support for multiple vault configurations
  • 🔄 Real-time monitoring with automatic re-indexing after file change
  • 🔁 Manual re-indexing on demand via the reindex_vaults tool
  • 🚀 Fast, incremental updates with ChromaDB backend
  • 🔒 Thread-safe operations for concurrent access
  • 🔧 Works as both MCP server and CLI tool

Prerequisites

  • Python 3.10 or higher
  • uv package manager

Installation

Install uv (if not already installed)

pip install uv

Install mcp-obsidian

Option 1: Install as a uv tool (Recommended)

uv tool install "git+https://github.com/alexhholmes/mcp-obsidian.git"
mcp-obsidian configure  # Configure your vaults

Option 2: Install from source

  1. Clone the repository:
git clone https://github.com/yourusername/mcp-obsidian.git
cd mcp-obsidian
  1. Create and activate a virtual environment with uv:
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
  1. Install the package in development mode:
uv pip install -e .

This will install all dependencies including:

  • questionary (interactive CLI)
  • chromadb (vector database)
  • langchain-text-splitters (document chunking)
  • fastmcp (MCP server framework)
  • watchdog (file system monitoring)

Configuration

Initial Setup

Configure your Obsidian vaults:

mcp-obsidian configure

This interactive command will:

  1. Prompt you to select vault directories
  2. Name each vault for easy reference
  3. Store configuration in ~/.mcp-obsidian/config.json

Manual Configuration

You can also manually edit ~/.mcp-obsidian/config.json:

{
  "vaults": [
    {
      "name": "Personal Notes",
      "path": "/path/to/your/obsidian/vault"
    },
    {
      "name": "Work Docs",
      "path": "/path/to/another/vault"
    }
  ]
}

Usage

As an MCP Server

Run the server for use with MCP-compatible clients:

mcp-obsidian

The server exposes the following tools:

  • semantic_search: Search across all configured vaults using semantic similarity with optional vault filtering
  • temporal_search: Search notes by modification date with optional semantic filtering
  • reindex_vaults: Manually trigger a re-index of all configured Obsidian vaults

The vectors are stored along with the following metadata, which can be used for filtering searches:

  • vault: The name of the vault containing the document
  • title: The filename without extension
  • source: The relative path from the vault root
  • modified: Unix timestamp of the file's last modification time
  • file_path: The absolute path to the source file
  • start_line / end_line: Line numbers for the chunk within the original document
  • chunk_index / total_chunks: Position of this chunk within the document
  • file_hash: MD5 hash of the file content for change detection

CLI Usage

Search directly from the command line:

# Search all vaults
mcp-obsidian search "your search query"

# Search a specific vault
mcp-obsidian search "your search query" --vault "Personal Notes"

# Reconfigure vaults
mcp-obsidian configure

# Rebuild search index
mcp-obsidian index

Integration with Claude Desktop

Add to your Claude Desktop configuration (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "obsidian": {
      "command": "mcp-obsidian"
    }
  }
}

or alternatively use to configuration tool to set it up automatically:

mcp-obsidian configure

How It Works

  1. Indexing: The server reads all markdown files from configured vaults and creates vector embeddings using ChromaDB
  2. Chunking: Large documents are split into smaller chunks using recursive character splitting for better search granularity
  3. Search: Queries are converted to embeddings and matched against the document database using cosine similarity
  4. File Watching: The server monitors vault directories for changes and automatically updates the index

License

MIT License

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