The full upstream README, mirrored here for reference. Install config, tool schemas, adoption signals, and an original overview live on the MCP Neo4j listing page.
These MCP servers are a part of the Neo4j Labs program. They are developed and maintained by the Neo4j Field GenAI team and welcome contributions from the larger developer community. These servers are frequently updated with new and experimental features, but are not supported by the Neo4j product team.
They are actively developed and maintained, but we don’t provide any SLAs or guarantees around backwards compatibility and deprecation.
If you are looking for the official product Neo4j MCP server please find it here.
Model Context Protocol (MCP) is a standardized protocol for managing context between large language models (LLMs) and external systems.
This lets you use Claude Desktop, or any other MCP Client (VS Code, Cursor, Windsurf, Gemini CLI), to use natural language to accomplish things with Neo4j and your Aura account, e.g.:
mcp-neo4j-cypher - natural language to Cypher queriesGet database schema for a configured database and execute generated read and write Cypher queries on that database.
Requirement: Requires the APOC plugin to be installed and enabled on the Neo4j instance for schema inspection.
mcp-neo4j-memory - knowledge graph memory stored in Neo4jStore and retrieve entities and relationships from your personal knowledge graph in a local or remote Neo4j instance. Access that information over different sessions, conversations, clients.
mcp-neo4j-cloud-aura-api - Neo4j Aura cloud service management APIManage your Neo4j Aura instances directly from the comfort of your AI assistant chat.
Create and destroy instances, find instances by name, scale them up and down and enable features.
mcp-neo4j-data-modeling - interactive graph data modeling and visualizationCreate, validate, and visualize Neo4j graph data models. Allows for model import/export from Arrows.app.
All servers support multiple transport modes:
To run a server in HTTP mode, use the --transport http flag:
Environment variables are also supported:
All servers in this repository are containerized and ready for cloud deployment on platforms like AWS ECS Fargate and Azure Container Apps. Each server supports HTTP transport mode specifically designed for scalable, production-ready deployments with auto-scaling and load balancing capabilities.
📋 Complete Cloud Deployment Guide →
The deployment guide covers:
Contributions are welcome! Please feel free to submit a Pull Request.
MIT License