martin-papy/qdrant-loader
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The Model Context Protocol (MCP) server is a crucial component for integrating AI development tools with vector databases, enabling intelligent search and retrieval capabilities.
The QDrant Loader MCP Server is designed to facilitate seamless integration between AI development tools and the QDrant vector database. By leveraging the Model Context Protocol, it provides a robust framework for retrieving and processing data in a way that enhances AI-driven workflows. The server supports real-time data processing and offers advanced search capabilities, making it an essential tool for developers looking to incorporate AI into their projects. With its compliance to MCP standards, the server ensures compatibility with popular AI tools like Cursor, Windsurf, and Claude Desktop, allowing for a streamlined development experience. The server's ability to handle large datasets and provide context-aware search results makes it particularly valuable for enterprise environments where data is often scattered across various platforms.
Features
- MCP protocol compliance: Ensures seamless integration with AI development tools.
- Advanced search tools: Offers semantic, hierarchy-aware, and attachment-focused search capabilities.
- Confluence intelligence: Provides deep understanding of page hierarchies and relationships.
- File attachment support: Comprehensive discovery of attachments with parent document context.
- Real-time processing: Enables streaming responses for large result sets.
Usages
Cursor IDE Integration
{
"mcpServers": {
"qdrant-loader": {
"command": "/path/to/venv/bin/mcp-qdrant-loader",
"env": {
"QDRANT_URL": "http://localhost:6333",
"QDRANT_COLLECTION_NAME": "my_docs",
"OPENAI_API_KEY": "your_key",
"MCP_DISABLE_CONSOLE_LOGGING": "true"
}
}
}
}