qdrant-mcp-server and qdrant-neo4j-crawl4ai-mcp
The two tools are ecosystem siblings, where B extends A's core functionality by integrating Neo4j and Crawl4AI with agentic RAG and a more robust enterprise architecture built on the A's foundation of using Qdrant and OpenAI embeddings for semantic search.
About qdrant-mcp-server
mhalder/qdrant-mcp-server
MCP server for semantic search using local Qdrant vector database and OpenAI embeddings
This tool helps software developers quickly find relevant code snippets, past changes, or project documentation by allowing them to ask questions in natural language. You feed it your codebase and Git history, and it produces highly relevant code, commits, or documents based on your queries, acting like a smart search engine for your development artifacts. It's designed for individual developers, team leads, or anyone needing to deeply understand or navigate large code repositories.
About qdrant-neo4j-crawl4ai-mcp
BjornMelin/qdrant-neo4j-crawl4ai-mcp
MCP server combining Qdrant vector search, Neo4j knowledge graphs, and Crawl4AI web intelligence with agentic RAG capabilities. FastMCP 2.0 architecture with enterprise security, monitoring, and Kubernetes deployment. AI/ML engineering powerhouse.
This is a ready-to-use server that helps you combine information from various sources to answer complex questions. You provide it with a question, and it gathers relevant content from websites, finds connections in your internal knowledge graphs, and uses semantic search to pull out key facts. The output is a more comprehensive and accurate answer, ideal for AI engineers building sophisticated AI assistants or automated analysis tools.
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