Neo4j MCP Server

Neo4j MCP Servers – Bridging AI Assistants and Graph Databases

The Neo4j Model Context Protocol (MCP) servers turn a graph database into an interactive, natural‑language workspace for AI assistants such as Claude. By exposing the full range of Neo4j capabilities through a standardized protocol, developers can query, manipulate, and manage their graph data without leaving the conversational UI of an MCP client. This solves a common pain point: the friction between natural language intent and the rigid syntax of graph query languages or cloud APIs. With MCP, a user can simply ask “Show me the top products by sales volume” or “Create a new Aura instance with 4 GB and Graph Data Science enabled”, and the assistant translates that into a precise Cypher query or cloud API call, executing it against Neo4j and returning the result in context.

The server suite is modular, with each component addressing a distinct need:

• converts spoken or typed intent into Cypher, fetching the database schema first to ensure query validity. It handles both read and write operations, making it ideal for exploratory data analysis or real‑time updates.
• provides a persistent knowledge‑graph layer that can be queried across sessions, enabling conversational agents to remember facts and relationships over time.
• lets the assistant act as a cloud‑management console for Neo4j Aura, allowing on‑demand instance creation, scaling, and feature toggling—all from chat.
• offers an interactive modeling interface that validates graph schemas and visualizes them, with import/export support for tools like Arrows.app.

Each server supports multiple transport modes—STDIO, SSE, and HTTP—so they can run locally for quick prototyping or be deployed as containerized microservices in AWS ECS Fargate, Azure Container Apps, or any HTTP‑capable cloud platform. The HTTP mode is especially valuable for production workloads: it supports streaming responses, load balancing, and auto‑scaling, ensuring that AI assistants can maintain low latency even under heavy query loads.

In practice, Neo4j MCP servers enable a variety of real‑world scenarios. Data scientists can let an AI assistant surface complex graph analytics without writing Cypher, while product managers can ask for visual dashboards of key metrics. DevOps teams can spin up or tear down Aura instances on demand, and knowledge‑graph curators can persist conversational context across multiple users and sessions. By abstracting the underlying database operations into a conversational layer, Neo4j MCP servers lower the barrier to entry for graph analytics and cloud management, allowing developers to focus on business logic rather than infrastructure details.