GraphRAG MCP Server

Overview

GraphRAG MCP is a Model Context Protocol server that unifies graph‑centric and vector‑based retrieval to give AI assistants a richer, more nuanced understanding of documents. By running on top of Neo4j and Qdrant, it turns a collection of text into a hybrid knowledge graph where each node is both a semantic vector and a part of an explicit relationship network. This dual representation allows Claude, Cursor, or any MCP‑enabled LLM to answer questions with both what is in the text and how it connects to other concepts.

The server solves a common pain point for developers building knowledge‑base assistants: the need to combine semantic similarity (which catches paraphrases and latent meaning) with structured relationships (which enforce logical consistency and enable multi‑step reasoning). Traditional vector search alone can retrieve relevant chunks, but it treats each piece of text as an isolated blob. Graph queries, on the other hand, excel at traversing relationships but lack a robust notion of semantic closeness. GraphRAG MCP bridges these worlds by exposing tools that first locate semantically similar chunks in Qdrant, then expand the context along Neo4j relationships to surface related entities, events, or hierarchical structures.

Key capabilities include:

• Semantic search () that returns the most embedding‑similar document chunks, optionally filtered by category or limit.
• Graph context expansion that walks outgoing relationships from a seed node, allowing the assistant to pull in related facts or provenance information.
• Hybrid search () that merges vector similarity with graph traversal, producing a ranked list of documents that are both semantically close and contextually connected.
• A fully documented Neo4j schema and Qdrant collection metadata, so developers can introspect the underlying data model without touching the code.

In practice, GraphRAG MCP shines in scenarios where knowledge must be retrieved with precision and depth. For example:

• Technical documentation assistants can pull in code snippets, API references, and related architectural diagrams by traversing the graph while still prioritizing semantic relevance.
• Legal or compliance bots can surface related statutes and case law by following citation relationships, ensuring that answers are both accurate and contextually grounded.
• Academic research tools can navigate citation networks while retrieving the most relevant abstracts or passages from related papers.

Integration is straightforward: once the MCP client (Claude Desktop, Cursor, etc.) registers the GraphRAG server, any LLM prompt can invoke the exposed tools via a simple function call. The server handles query parsing, database communication, and result formatting internally, freeing developers to focus on higher‑level conversational logic. Its modular design also means that the graph or vector backend can be swapped out without changing client code, giving teams flexibility as their data infrastructure evolves.

Overall, GraphRAG MCP offers developers a powerful, low‑friction pathway to embed sophisticated hybrid retrieval into AI assistants, enabling answers that are not only semantically relevant but also richly connected to the underlying knowledge graph.