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Ingest and query structured and unstructured data across graphs, vectors, and LLMs.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "KGrag MCP Server" yet — see the docs or source repo.
Use KGrag MCP Server to connect my graph database and vector index, ingest product docs, FAQs, and meeting notes, and provide a query flow that returns answers with citations.
A data ingestion plan, indexing approach, and a citation-aware Q&A query workflow.
I have structured sales data and unstructured customer interview notes. Using KGrag MCP Server, design a unified query approach so I can retrieve metric changes alongside related customer feedback.
A multi-source linking strategy, sample queries, and a combined output format for metrics and text evidence.
Design a graph-enhanced QA workflow with KGrag MCP Server: first find relevant nodes in an entity relationship graph, then combine vector-retrieved passages so the LLM can generate more accurate answers.
An end-to-end QA workflow covering graph retrieval, vector recall, and LLM generation.
Ingest documents into Neo4j to build and query a knowledge graph.
Query and retrieve data across GitHub, Neo4j, PostgreSQL, and Milvus.
Provides extensible MCP access to LangGraph documentation and resources.
Provide persistent graph memory, semantic search, and traversal for AI agents.
Build a semantic graph from project files for search, knowledge, and task management.
Connect to Grafana dashboards, data sources, and alerts for monitoring analysis.