Access Apache Solr indexes via Model Context Protocol with hybrid keyword and vector search, optimized vector embeddings, and efficient SQL-filtered vector queries for enhanced AI assistant integration and high-performance semantic search.
Unlock the full potential of Solr MCP through LangDB's AI Gateway. Get enterprise-grade security, analytics, and seamless integration with zero configuration.
Free tier available • No credit card required
A Python package for accessing Apache Solr indexes via Model Context Protocol (MCP). This integration allows AI assistants like Claude to perform powerful search queries against your Solr indexes, combining both keyword and vector search capabilities.
The system employs an important optimization for combined vector and SQL queries. When executing a query that includes both vector similarity search and SQL filters:
This optimization reduces computational overhead and network transfer by minimizing the number of vector similarity calculations needed.
docker-compose up -d
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate pip install poetry poetry install
python scripts/process_markdown.py data/bitcoin-whitepaper.md --output data/processed/bitcoin_sections.json python scripts/create_unified_collection.py unified python scripts/unified_index.py data/processed/bitcoin_sections.json --collection unified
poetry run python -m solr_mcp.server
For more detailed setup and usage instructions, see the QUICKSTART.md guide.
This project is licensed under the MIT License - see the LICENSE file for details.
Contributions are welcome! Please see CONTRIBUTING.md for guidelines.
Discover shared experiences
Shared threads will appear here, showcasing real-world applications and insights from the community. Check back soon for updates!