io.github.neo4j-contrib/mcp-neo4j-data-modeling MCP Server
io.github.neo4j-contrib/mcp-neo4j-data-modeling
Create, validate, and visualize Neo4j graph data models with interactive tools and Arrows.app integration.
What is the io.github.neo4j-contrib/mcp-neo4j-data-modeling MCP server?
The mcp-neo4j-data-modeling MCP server enables interactive creation, validation, and visualization of Neo4j graph data models. It supports importing and exporting models from Arrows.app, allowing you to design and refine graph schemas through natural language commands in Claude, Cursor, or other MCP clients.
This server lets you design and manage Neo4j graph data models visually and programmatically. You can create new models, validate their structure, visualize relationships, and seamlessly import/export designs from Arrows.app—all through conversational AI without leaving your chat interface.
How to install io.github.neo4j-contrib/mcp-neo4j-data-modeling
Copy-paste configuration for popular MCP clients.
NEO4J_NAMESPACEThe namespace to use for the MCP server tool names.
Tools & capabilities
Tools this server exposes to the agent.
Create graph data model— Design and create new Neo4j graph data models interactivelyValidate data model— Validate the structure and integrity of graph data modelsVisualize graph model— Generate visual representations of graph data modelsImport from Arrows.app— Import graph data models from Arrows.appExport to Arrows.app— Export graph data models to Arrows.app for further editing
Use cases
- Design a new graph schema by describing entities and relationships in natural language
- Validate an existing data model for consistency and best practices
- Visualize complex graph structures to understand relationships and dependencies
- Import a model from Arrows.app and refine it through conversation
- Export your designed model to Arrows.app for collaborative editing or presentation
io.github.neo4j-contrib/mcp-neo4j-data-modeling MCP server FAQ
It's an MCP server that lets you create, validate, and visualize Neo4j graph data models interactively. You can design schemas through natural language, import/export from Arrows.app, and work with models across Claude Desktop, Cursor, and other MCP clients.
Install via PyPI: `pip install mcp-neo4j-data-modeling`. Then configure it in your MCP client (Claude Desktop, Cursor, etc.) to enable the server.
The README focuses on model creation and visualization; check the server-specific documentation for whether a live database connection is required for all features.
Yes, the server supports importing graph data models from Arrows.app and exporting them back for collaborative design and refinement.
The server supports STDIO (default for local/Claude Desktop), SSE (Server-Sent Events), and HTTP (for web and cloud deployments with custom host/port configuration).
No, it's part of the Neo4j Labs program developed by the Field GenAI team. It's actively maintained but not officially supported by Neo4j product team, and backwards compatibility is not guaranteed.
README (reference)
Source of truth, from the repository.
Neo4j Labs MCP Servers
Neo4j Labs
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.
Overview
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.:
- 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
Servers
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 APOC plugin to 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 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 visualization
Create, validate, and visualize Neo4j graph data models. Allows for model import/export from Arrows.app.
Transport Modes
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
HTTP Transport Configuration
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
Cloud Deployment
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:
- 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
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
Blog Posts
- Everything a Developer Needs to Know About the Model Context Protocol (MCP)
- Claude Converses With Neo4j Via MCP - Graph Database & Analytics
- Building Knowledge Graphs With Claude and Neo4j: A No-Code MCP Approach - Graph Database & Analytics
- Using the Neo4j Extension in Gemini CLI
License
MIT License
Related MCP servers
io.github.neo4j-contrib/mcp-neo4j-memory
Store and retrieve personal knowledge graphs in Neo4j across sessions and clients.
Neo4j GDS Agent
Run Neo4j Graph Data Science algorithms through Claude, Cursor, and other LLMs via MCP.
Manage Neo4j Aura database instances via natural language through Claude and other MCP clients.
io.github.neo4j-contrib/mcp-neo4j-cypher
Run Cypher queries against Neo4j databases using natural language.
Opinionated sprint tracker. Read/update tickets, sprints, velocity from Claude/Cursor/Zed.