Neo4j

- **Query your Neo4j graph schema**— inspect node labels, relationship types, and property keys using`get_schema`. - **Run read-only Cypher queries**— ask natural-language questions and let the assistant execute`READ`queries against your database. - **Execute write Cypher statements**— create, update, or delete graph data via`WRITE`queries generated from your instructions. - **Store facts as a knowledge graph**— save entities and relationships to a persistent memory graph with`mcp-neo4j-memory`and recall them across sessions. - **Manage Neo4j Aura cloud instances**— list, create, resize, or destroy Aura instances and toggle features through`mcp-neo4j-cloud-aura-api`. - **Design and visualize graph data models**— create, validate, and export Neo4j data models, including import/export from Arrows.app. These MCP servers are a part of the[Neo4j Labsprogram. 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](https://neo4j.com/labs/)[here. Model Context Protocol (MCP) is a](https://github.com/neo4j/mcp)[standardized protocolfor 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.: - What is in this graph? - Render a chart from the top products sold by frequency, total and average volume - List my instances - Create a new instance named mcp-test for Aura Professional with 4GB and Graph Data Science enabled - Store the fact that I worked on the Neo4j MCP Servers today with Andreas and Oskar ### `mcp-neo4j-cypher`- natural language to Cypher queries Get database schema for a configured database and execute generated read and write Cypher queries on that database. **Requirement**: Requires the](https://modelcontextprotocol.io/introduction)[APOC pluginto be installed and enabled on the Neo4j instance for schema inspection. ### `mcp-neo4j-memory`- knowledge graph memory stored in Neo4j Store 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 API Manage your](https://neo4j.com/docs/apoc/current/installation/)[Neo4j Aurainstances 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 visualization Create, validate, and visualize Neo4j graph data models. Allows for model import/export from Arrows.app. All servers support multiple transport modes: - **STDIO**(default): Standard input/output for local tools and Claude Desktop integration - **SSE**: Server-Sent Events for web-based deployments - **HTTP**: Streamable HTTP for modern web deployments and microservices To run a server in HTTP mode, use the`--transport http`flag: ``` `# Basic HTTP mode mcp-neo4j-cypher --transport http # Custom HTTP configuration mcp-neo4j-cypher --transport http --host 127.0.0.1 --port 8080 --path /api/mcp/` ``` Environment variables are also supported: ``` `export NEO4J_TRANSPORT=http export NEO4J_MCP_SERVER_HOST=127.0.0.1 export NEO4J_MCP_SERVER_PORT=8080 export NEO4J_MCP_SERVER_PATH=/api/mcp/ mcp-neo4j-cypher` ``` 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. - **AWS ECS Fargate**: Step-by-step deployment with auto-scaling and Application Load Balancer - **Azure Container Apps**: Serverless container deployment with built-in scaling and traffic management - **Configuration Best Practices**: Security, monitoring, resource recommendations, and troubleshooting - **Integration Examples**: Connecting MCP clients to cloud-deployed servers Contributions are welcome! Please feel free to submit a Pull Request. - ](https://console.neo4j.io)[Everything a Developer Needs to Know About the Model Context Protocol (MCP) - ](https://neo4j.com/blog/developer/model-context-protocol/)[Claude Converses With Neo4j Via MCP - Graph Database & Analytics - ](https://neo4j.com/blog/developer/claude-converses-neo4j-via-mcp/)[Building Knowledge Graphs With Claude and Neo4j: A No-Code MCP Approach - Graph Database & Analytics - ](https://neo4j.com/blog/developer/knowledge-graphs-claude-neo4j-mcp/)[Using the Neo4j Extension in Gemini CLI Integrate the Neo4j graph database with clients through natural language interactions. Query and interact with FalkorDB graph databases using AI models. Query a Neo4j graph database containing a knowledge graph of German family businesses. Query a hybrid graph (Neo4j) and vector (Qdrant) database for powerful semantic and graph-based document retrieval. Connects to Neo4j graph databases with ability to use GDS functions ( when available), a read only mode , and set the sample size for schema detection A scalable knowledge graph memory system for LLMs with semantic retrieval and temporal awareness, using Neo4j as a backend. MCP server for Neo4j — run Cypher queries, explore schema, and inspect database info over stdio A knowledge graph memory server using the Neo4j graph database to store and retrieve information from AI interactions. Interact with and explore graph data in a Neo4j database.](https://cloud.google.com/blog/topics/developers-practitioners/using-the-neo4j-extension-in-gemini-cli)