MCP Neo4J Server (SSE & STDIO)

The MCP Neo4J Server is a Java‑based implementation of the Model Context Protocol that exposes Neo4j graph databases to AI assistants. By leveraging Server‑Sent Events (SSE) or STDIO as transport, it allows assistants to execute Cypher queries, explore schemas, and ingest sample data—all without leaving the conversational flow. This solves a common pain point for developers: integrating complex graph analytics into AI‑driven workflows while maintaining a consistent, low‑latency communication channel.

At its core, the server registers six high‑level tools that map directly to common graph operations. The and tools let an assistant read from or mutate the graph, returning results in JSON form or a concise summary of changes. The tool provides an introspective view of node labels, properties, and relationships, enabling dynamic schema discovery during a conversation. These tools are automatically wired through Spring AI’s annotation, ensuring that the server is ready to serve as soon as it starts.

For developers building AI‑enhanced data products, this server offers several compelling advantages. First, the SSE mode provides a non‑blocking, event‑driven channel that is ideal for real‑time dashboards or continuous query streams. Second, the STDIO mode enables lightweight local deployments—perfect for testing or CI pipelines where a networked server is unnecessary. Third, the integration with guarantees that the server follows best practices for reactive programming, giving assistants low‑latency responses even under load.

Real‑world scenarios include building a conversational analytics platform where users can ask natural language questions that are translated into Cypher, or creating an AI‑powered data exploration tool that suggests new relationships and visualizations on the fly. Because the server exposes a clean MCP interface, any client that understands MCP—such as Claude or other AI assistants—can seamlessly invoke graph queries, retrieve schema information, and even generate sample data through the provided prompt. This tight coupling between AI logic and graph storage removes friction from the development cycle, allowing teams to iterate quickly on data‑centric applications.