Building GraphRAG Python MCP tools
Build a custom MCP server in Python with FastMCP. Create Neo4j graph-backed tools, resources, and prompts that ground AI agents with GraphRAG.
In this 2-hour course, you will learn
Building 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. Python MCP tools teaches you how to build your own Model Context Protocol (MCP) server in Python, with tools, resources, and prompts backed by a Neo4j graph database. In around two hours you will go from an empty project to a server that any MCP client can use to query a graph in natural language.
Developing with Neo4j MCP Tools showed you how to consume an existing MCP server. This course puts you on the other side of the protocol. Using the MCP Python SDK and its FastMCP server, you will define the tools an AI agent calls, manage the Neo4j driver behind them, and shape what the agent sees so it can answer questions accurately from your data.
FastMCP Server Development
Build a fully functional MCP server in Python using FastMCP, defining tools, resources, and prompts that AI agents can invoke at runtime.
Neo4j Driver Lifecycle Management
Configure and manage the Neo4j Python driver correctly within a FastMCP server, handling connection setup, session scoping, and teardown safely.
Context-Aware Tools with Logging
Write MCP tools that accept structured context, emit structured log messages, and surface meaningful progress information back to the calling agent.
Pagination for Large Datasets
Implement cursor-based pagination in Neo4j-backed tools so agents retrieve large graph result sets in manageable chunks without overwhelming memory.
Text-to-Cypher Natural Language Queries
Apply an LLM-powered text-to-Cypher pipeline so agents can query the Neo4j graph using plain English instead of hand-written Cypher statements.
3 modules, 2 hours.
Who this course is for
This course is for Python developers building GenAI applications who need agents to do more than an off-the-shelf server allows. Generic database tools are a good start, but real applications need domain-specific capabilities: a tool that encapsulates a known-good query, a resource that exposes reference data, a prompt that guides the agent through a workflow. It also suits developers who learn best by building - every concept is introduced briefly and then applied in a challenge. You should be comfortable with Python and the command line, and understand large language models, grounding, and the MCP host, client, and server concepts before you start; the prerequisite courses are listed beside this overview.
What you'll build and do
You will build a complete MCP server, feature by feature. Starting from a minimal FastMCP application, you will add a Neo4j connection with a properly managed driver lifecycle, then build graph-backed tools on top of it, including a 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. tool that retrieves data from the movie graph to ground an agent's answers. You will use the context object to add logging and progress reporting, expose read-only data as resources, handle large result sets with pagination, write prompts that package reusable instructions for clients, and explore text-to-Cypher, where the agent turns a natural language question into a query against your graph.
Each feature ends with a challenge in which you implement it yourself and verify it works from a real MCP client. You need Python and the uv package manager installed, plus a GitHub account with a Copilot plan enabled - the challenges use GitHub Copilot as the MCP client. GraphAcademy provides an online Neo4j instance loaded with a movie recommendations dataset.
Where to go next
If your team works in TypeScript, Building GraphRAG TypeScript MCP tools covers the same server-building journey with the MCP TypeScript SDK. To go deeper into the data side, Building Knowledge Graphs with LLMs teaches 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. your tools query, and Constructing Knowledge Graphs with Neo4j GraphRAG for Python takes a code-first approach to the same problem. This course also prepares you for the Neo4j & Generative AI Certification, which validates your ability to build GenAI applications backed by Neo4j.