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Ingest documents into Neo4j to build and query a knowledge graph.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "GraphRAG MCP Server" yet — see the docs or source repo.
Please ingest the PDF and Markdown documents in this project folder into the GraphRAG knowledge base, extract people, organizations, concepts, and their relationships, and return a summary of the ingestion results.
Returns ingestion status, counts of key extracted entities and relationships, and a summary of the knowledge graph built.
Query the GraphRAG knowledge base for the relationships among Gemini, Neo4j, and entity extraction. Give me a structured answer and cite the relevant source documents.
Outputs relevant entities, relationship paths, concise explanations, and citations to the corresponding source documents.
Please check the health status of the GraphRAG MCP Server, including graph database connectivity, knowledge base availability, and whether the most recent ingestion completed successfully.
Returns service health check results, highlighting connectivity, availability, and any potential issues.
Provide persistent graph memory, semantic search, and traversal for AI agents.
Query and explore Neo4j graph databases with natural language and Cypher.
Build advanced RAG retrieval with knowledge graphs, multimodal parsing, and flexible query modes.
Access GraphRAG research, implementation patterns, and best practices for building RAG systems.
Ingest and query structured and unstructured data across graphs, vectors, and LLMs.
Turn PostgreSQL data into a Neo4j graph for natural language querying.