Neo4j Remote MCP Server

Overview

The MCP Neo4J Server Sse is a specialized Model Context Protocol server that bridges AI assistants with graph databases through Server‑Sent Events (SSE) or standard input/output streams. By exposing Neo4j’s powerful Cypher query language as first‑class tools, it allows developers to embed complex graph analytics directly into AI workflows without managing database drivers or handling low‑level connectivity themselves. This server is particularly valuable for data scientists, knowledge engineers, and AI developers who need to query relational or hierarchical data structures on demand.

At its core, the server provides three families of tools. Read‑Neo4j‑Cypher executes arbitrary read queries and returns structured results, enabling AI agents to retrieve facts or relationships on the fly. Write‑Neo4j‑Cypher performs updates, returning a concise summary of affected nodes and relationships so that agents can track state changes. Get‑Neo4j‑Schema exposes the graph’s schema—node labels, properties, and inter‑label relationships—allowing agents to introspect the database layout before crafting queries. Together, these tools give an AI assistant a full read‑write interface to Neo4j, mirroring the experience of native drivers but within the MCP ecosystem.

The server’s SSE transport mode offers a lightweight, event‑driven channel that is ideal for real‑time dashboards or streaming analytics. In contrast, the STDIO mode supports local development and testing where a persistent network connection is unnecessary or unavailable. Developers can configure the server through simple JSON snippets in their cline_mcp_settings.json, selecting either mode and providing connection details such as bolt URL, credentials, and target database. This plug‑in style integration means an AI assistant can be extended to talk to any Neo4j instance with minimal friction.

Typical use cases include knowledge graph exploration, recommendation engines, fraud detection pipelines, and semantic search backends. An AI assistant can query a product graph to surface related items, update inventory nodes in response to user actions, or retrieve the schema to generate dynamic forms. In research settings, scientists can interrogate biological networks or citation graphs directly from conversational agents, turning complex graph queries into natural language interactions.

What sets this MCP server apart is its dual‑mode transport, enabling both cloud‑scale event streams and local debugging workflows. The server’s design follows the MCP specification closely, ensuring compatibility with existing AI assistants and client libraries. By abstracting Neo4j’s Cypher language behind well‑defined tools, it empowers developers to harness graph intelligence without the overhead of database integration, making advanced data exploration accessible within conversational AI applications.