MCP Server for Semantic Search in Obsidian Vaults

✍️ OpenClawRadar📅 Published: March 30, 2026🔗 Source
MCP Server for Semantic Search in Obsidian Vaults
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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
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Compatibility and Availability

The server works with Claude Code, Cursor, Windsurf, or any MCP-compatible agent. It's available on GitHub and PyPI:

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

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👀 See Also