obsidian-mcp

MCP server for Obsidian vaults with hybrid semantic search.

Features

• Hybrid search: BM25 + Voyage AI embeddings + Cohere reranking

• Backlinks: Pre-computed wikilink graph

• Similar notes: Discover hidden connections

• Full CRUD: Create, read, update, delete, move notes

• Live indexing: Index notes directly from Claude Code without restart

Related MCP server: Obsidian MCP Server

Installation

cd obsidian-mcp
uv sync

Configuration

1. Copy .env.example to .env and add your API keys:

VOYAGE_API_KEY=your_key
COHERE_API_KEY=your_key  # optional

2. Set your vault path in config/settings.yaml:

vault:
  path: "D:\\path\\to\\your\\vault"

Usage

Recommended Workflow (via MCP)

The recommended workflow is to use the MCP tools directly from Claude Code:

1. write   -> Create or modify a note (auto-indexes by default)
2. search  -> Note is instantly searchable

Notes are automatically indexed after write, so manual indexing is only needed for:

• Notes created manually in Obsidian (use index tool)

• Bulk imports (use index with full=True)

Interactive Menu

python obsidian-menu.py

CLI

# Full indexation
python obsidian-cli.py --vault "/path/to/vault" index --full

# Incremental indexation
python obsidian-cli.py --vault "/path/to/vault" index

# Status
python obsidian-cli.py --vault "/path/to/vault" status

# Search
python obsidian-cli.py --vault "/path/to/vault" search "query"

Note: CLI indexation requires a Claude Code restart for changes to be visible in MCP searches due to ChromaDB caching. Use the index MCP tool instead for live updates.

Claude Code Configuration

Option 1: HTTP/SSE Server (Recommended)

Run as a shared HTTP server to avoid duplicating processes across Claude sessions.

1. Start the HTTP server:

uv run python -m src.server_http
# Server runs on http://127.0.0.1:8322/mcp

2. Configure Claude Code (~/.claude.json):

{
  "mcpServers": {
    "obsidian": {
      "type": "sse",
      "url": "http://127.0.0.1:8322/mcp"
    }
  }
}

3. (Optional) Run as systemd service (Linux):

# Create ~/.config/systemd/user/obsidian-mcp.service
[Unit]
Description=Obsidian MCP HTTP Server
After=network.target

[Service]
Type=simple
WorkingDirectory=/path/to/obsidian-mcp
Environment="VOYAGE_API_KEY=your_key"
Environment="COHERE_API_KEY=your_key"
Environment="OBSIDIAN_VAULT=/path/to/vault"
ExecStart=/path/to/uv run python -m src.server_http
Restart=on-failure

[Install]
WantedBy=default.target

# Enable and start
systemctl --user daemon-reload
systemctl --user enable --now obsidian-mcp

Option 2: Stdio (per-session)

Add to ~/.claude.json:

{
  "mcpServers": {
    "obsidian": {
      "command": "uv",
      "args": ["--directory", "/path/to/obsidian-mcp", "run", "python", "-m", "src.server"],
      "env": {
        "VOYAGE_API_KEY": "your_key",
        "COHERE_API_KEY": "your_key",
        "OBSIDIAN_VAULT": "/path/to/vault"
      }
    }
  }
}

MCP Tools

Auto-Indexing (Write Tool)

By default, write automatically indexes the note after creating or modifying it:

# Auto-indexes after writing (default)
write(path="Notes/my_note.md", content="...")

# Disable auto-indexing if needed
write(path="Notes/my_note.md", content="...", auto_index=False)

This makes new notes instantly searchable without manual indexing.

Index Tool

The index tool indexes notes directly within the MCP server process, avoiding ChromaDB cache synchronization issues:

# Incremental indexation (default)
index()  # Only indexes new/modified notes

# Full reindexation
index(full=True)  # Reindexes all notes

Why use index instead of CLI?

ChromaDB maintains an HNSW index cache per process. When you index via CLI (separate process), the MCP server doesn't see the changes until restart. The index tool solves this by:

1. Running indexation in the same process as the MCP server

2. Resetting internal caches after indexation

3. Making new notes immediately searchable

Project Structure

obsidian-mcp/
  src/
    server.py           # MCP server (11 tools)
    indexer.py          # Indexation engine
    retriever.py        # Hybrid search (BM25 + semantic + RRF)
    note_parser.py      # Obsidian markdown parser
    wikilink_graph.py   # Backlink graph
  config/
    settings.yaml       # Configuration
  chroma_db/            # Vector database (ChromaDB)
  obsidian-cli.py       # CLI interface
  obsidian-menu.py      # Interactive menu

Technical Details

Hybrid Search Pipeline

1. BM25: Lexical search for exact term matching

2. Semantic: Voyage AI embeddings (voyage-3) for meaning

3. Fusion: Reciprocal Rank Fusion (RRF) combines results

4. Reranking: Cohere reranker (rerank-v4.0-pro) for final ordering

Embedding Model

• Model: voyage-3

• Dimensions: 1024

• Provider: Voyage AI

Database

• ChromaDB with persistent storage

• SQLite backend with WAL mode

• HNSW index for fast similarity search

License

MIT