Developing with Neo4j MCP Tools
Learn to connect AI agents to Neo4j with the Model Context Protocol. Install the Neo4j MCP server, query graphs in natural language, and build apps.
In this 2-hour course, you will learn
Developing with Neo4j MCP Tools teaches you how to connect AI agents to a Neo4j database using the Model Context Protocol (MCP). In around two hours you will install the Neo4j MCP server, explore a graph in natural language, and use MCP tools to build a working application.
The Model Context Protocol is an open standard that gives AI agents a consistent way to discover and call tools. Instead of writing a bespoke integration for every model and every data source, you expose your data once as MCP tools, and any MCP host - GitHub Copilot, Claude, Cursor, and others - can use them. An agent connected to the Neo4j MCP server can inspect the database schema, generate Full definition for Cypher (opens in a new tab)Neo4j's implementation of GQL, the ISO standard query language for graph databases. It is declarative: you describe the pattern to find, and the database decides how to find it. from a plain-English question, run the query, and reason over the results as part of its answer.
Model Context Protocol Fundamentals
Understand how the Model Context Protocol works, what tools and resources it exposes, and how AI agents use MCP to interact with external systems like Neo4j.
Neo4j MCP Server Installation
Configure and install the Neo4j MCP server locally, connect it to a running Neo4j instance, and verify that an AI agent can discover and invoke its tools.
Natural Language Database Interaction
Write natural language prompts that an AI agent translates into Cypher queries, letting you explore and interrogate a Neo4j graph without writing Cypher directly.
Building GraphRAG Applications
Build retrieval-augmented generation applications that combine Neo4j graph context with large language models to produce accurate, graph-grounded AI responses.
2 modules, 2 hours.
Who this course is for
This course is for developers and data practitioners who want AI agents to work directly with graph data - whether you are building agentic systems that need to answer questions from a live database, or evaluating MCP itself and want to see the host, client, and server architecture working end to end. You do not need to be an AI specialist, but you should be able to read a Full definition for Cypher (opens in a new tab)Neo4j's implementation of GQL, the ISO standard query language for graph databases. It is declarative: you describe the pattern to find, and the database decides how to find it. statement and understand large language models, grounding, and Full definition for GraphRAG (opens in a new tab)Retrieval-augmented generation whose context comes from a knowledge graph, so the model can follow the relationships between facts. before you start; the prerequisite courses are listed beside this overview.
What you'll build and do
This is a hands-on course built around challenges, not demonstrations. You will install the
mcp-neo4j-cypherserver, register it with GitHub Copilot in Visual Studio Code, and connect it to an online Neo4j instance that GraphAcademy provides, preloaded with a movie recommendations dataset. You will need a GitHub account with a Copilot plan enabled, but you do not need to install a database.From that point on, you work through the agent: you use the schema tool to let it discover what is in the database, generate Full definition for Cypher (opens in a new tab)Neo4j's implementation of GQL, the ISO standard query language for graph databases. It is declarative: you describe the pattern to find, and the database decides how to find it. statements from natural language questions - for example, finding the highest-rated movie in the dataset - and check the queries the agent produces. The course ends with a larger challenge in which you build a complete application on top of the movie graph, with the agent using MCP tools to explore the schema, test queries, and write the code.
Where to go next
Using an existing MCP server is only half the story. When you want an agent to call your own domain logic, the next step is to build a custom server: Building GraphRAG Python MCP tools shows you how with the MCP Python SDK, and Building GraphRAG TypeScript MCP tools covers the same ground with the TypeScript SDK. For the broader picture of grounding Go to glossary for large language model (opens in a new tab)A model trained on text to predict the next token, and so to generate language. in graph data, Building Knowledge Graphs with LLMs shows you how to construct the Full definition for knowledge graph (opens in a new tab)A representation of real-world entities and their relationships, stored according to organizing principles, typically in a graph database. an agent queries. This course also counts toward the Neo4j & Generative AI Certification, which validates your ability to build GenAI applications backed by Neo4j.