Memgraph MCP Server

The Memgraph MCP Server is a lightweight, protocol‑first bridge that brings the power of a high‑performance graph database into the world of large language models. By implementing the Model Context Protocol (MCP), it exposes Memgraph’s Cypher query engine, schema metadata, and related utilities as a set of discoverable tools that any MCP‑compliant client—such as Claude, GPT‑4o, or custom agents—can invoke on demand. This eliminates the need for bespoke SDKs or manual API wrappers, allowing developers to treat graph queries as first‑class actions within conversational workflows.

At its core, the server offers two primary capabilities: run_query() and get_schema(). The former lets an LLM send arbitrary Cypher statements directly to Memgraph, receiving structured results that can be parsed or visualized by downstream components. The latter provides schema introspection, enabling an assistant to understand node labels, relationship types, and property definitions before constructing queries. Together, these tools empower assistants to reason about graph structure, generate context‑aware queries, and present results in natural language or visual formats.

For developers building intelligent agents that rely on relational knowledge, this MCP integration is invaluable. It removes the overhead of writing custom HTTP endpoints or managing authentication flows; instead, the MCP client handles session management and request routing. Real‑world scenarios include a customer support bot that can fetch ticket relationships, a recommendation engine that traverses user–item graphs on the fly, or an analytics assistant that summarizes graph statistics in plain English. Because MCP treats each tool as a declarative contract, developers can rapidly iterate on query logic without redeploying code.

The server’s design is intentionally minimalistic yet extensible. It already supports running Memgraph in Docker with schema‑info enabled, making it straightforward to spin up a test environment. Future releases will add a TypeScript variant and deeper integration with the central Memgraph AI Toolkit, positioning it as part of a unified ecosystem that spans LangChain, LlamaIndex, and other modern frameworks. By standardizing how AI assistants interact with graph data, the Memgraph MCP Server lowers the barrier to entry for building graph‑centric applications and accelerates the adoption of knowledge graphs in conversational AI.