mcp-obsidian

An MCP (Model Context Protocol) server for semantic search in Obsidian vaults using embedded ChromaDB vector storage.

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)

Install mcp-obsidian

1. Clone the repository:

2. Create and activate a virtual environment with uv:

3. Install the package in development mode:

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:

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:

Usage

As an MCP Server

Run the server for use with MCP-compatible clients:

The server exposes the following tools:

• search_notes: Search across all configured vaults with semantic matching

• search_vault: Search within a specific vault

CLI Usage

Search directly from the command line:

Integration with Claude Desktop

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

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