A knowledge graph feeds your AI context that boosts its accuracy and explainability. Then you can build an intelligent agent able to carry out tasks more reliably.

However, graph data modeling requires a different mindset than other databases. You should focus only on relationships relevant to your specific use cases. Otherwise, you risk overcomplicating your model, which will bog down queries and slow performance.

Thankfully, the Model Context Protocol (MCP) can provide the context needed to improve an AI’s reasoning abilities, so that it can assist and make graph data modeling easier.

Join us on November 20th for a 30-minute rundown of how this works using the open-source MCP Server for Neo4j.  

You’ll learn how to:

• Deploy it yourself
• Query context for AI-assisted graph data modeling
• Speed up your development workflow