MCP Server for Semantic Search in Obsidian Vaults
A developer has created an MCP server that provides semantic search capabilities for Obsidian vaults, addressing the problem of agents missing relevant documents due to keyword matching limitations.
Key Features
- Indexes Obsidian vaults into Qdrant vector database with local embeddings
- Uses BAAI/bge-small-en-v1.5 embedding model (384 dimensions, no API keys required)
- Chunks markdown by headings without breaking tables or code blocks
- Auto-starts Qdrant via Docker if not already running
- Supports filtering by project, document type, or frontmatter tags
- Implements incremental indexing - only re-embeds changed files
- Returns only relevant chunks rather than entire files
- Maintains fast performance even with large vaults containing hundreds of files
Compatibility and Availability
The server works with Claude Code, Cursor, Windsurf, or any MCP-compatible agent. It's available on GitHub and PyPI:
- GitHub: https://github.com/Marco-O94/obsidian-qdrant-search
- PyPI: https://pypi.org/project/obsidian-qdrant-search/
The developer is seeking feedback on chunking strategies, embedding model choices, and bug reports, noting that edge cases may not yet be covered.
📖 Read the full source: r/ClaudeAI
👀 See Also
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