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.