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claudiogarza

Obsidian RAG MCP Server

by claudiogarza
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Python
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Obsidian RAG MCP Server

Semantic search for your Obsidian vault, exposed as an MCP server for Claude Code integration.

What This Does

Turn your Obsidian vault into a searchable knowledge base that AI assistants can query semantically. Instead of keyword matching, find documents by meaning.

Example:

"Find RCAs where database timeouts caused customer-facing issues"

Returns relevant RCA documents even if they use terms like "CosmosDB latency", "connection pool exhaustion", or "query timeout" — not just exact keyword matches.

Features

  • 🔍 Semantic Search: Find documents by meaning, not just keywords

  • 🏷️ Tag-Aware: Filter searches by Obsidian tags

  • 📊 Metadata Extraction: Leverages frontmatter and document structure

  • 🔌 MCP Integration: Works directly with Claude Code

  • 🏠 Local-First: Your data stays on your machine (only embeddings sent to OpenAI)

  • ⚡ Fast: Sub-second queries on 100+ document vaults

Quick Start

Prerequisites

  • Python 3.11+

  • OpenAI API key (for embeddings)

  • An Obsidian vault (or use the included sample vault)

Installation

# Clone the repository git clone https://github.com/yourusername/obsidian-rag-mcp.git cd obsidian-rag-mcp # Create virtual environment python -m venv .venv source .venv/bin/activate # or `.venv\Scripts\activate` on Windows # Install dependencies pip install -e . # Set up environment cp .env.example .env # Edit .env and add your OPENAI_API_KEY

Index Your Vault

# Index the sample vault obsidian-rag index ./vault # Or index your own vault obsidian-rag index /path/to/your/obsidian/vault

Test It

# Search from CLI obsidian-rag search "database connection issues" # Start the MCP server obsidian-rag serve

Connect to Claude Code

Add to your Claude Code MCP configuration:

{ "mcpServers": { "obsidian-rag": { "command": "obsidian-rag", "args": ["serve", "--vault", "/path/to/your/vault"], "env": { "OPENAI_API_KEY": "your-key-here" } } } }

MCP Tools

Once connected, Claude Code has access to these tools:

Tool

Description

search_vault

Semantic search across all vault content

search_by_tag

Search filtered by Obsidian tags

get_related

Find notes related to a given note

get_note

Retrieve full content of a specific note

list_recent

List recently modified notes

Sample Vault

The vault/ directory contains sample RCA documents for testing. To regenerate:

python scripts/seed_vault.py

Architecture

See docs/ARCHITECTURE.md for detailed system design.

Development

# Install dev dependencies pip install -e ".[dev]" # Run tests pytest # Type checking mypy src # Linting ruff check src

Configuration

Environment variables:

Variable

Required

Description

OPENAI_API_KEY

Yes

OpenAI API key for embeddings

OBSIDIAN_VAULT_PATH

No

Default vault path

CHROMA_PERSIST_DIR

No

ChromaDB storage location

Cost

Embedding costs with OpenAI text-embedding-3-small:

  • ~100 notes (avg 2000 tokens each): ~$0.02 per full reindex

  • Queries: ~$0.00001 per query

License

MIT

Acknowledgments

This server cannot be installed

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security - not tested
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quality - not tested

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