FastMCP Tutorial: Quick Tools, Live Agents, Real Results with LangGraph

Looking for a practical FastMCP tutorial that goes beyond theory and shows you how to build real AI agent workflows? This webinar will provide a hands-on introduction to FastMCP, the fastest way to build MCP-compatible tools and services for modern AI agents.

As AI agents become more capable, the need for standardized ways to connect them to tools, data sources, and external systems is growing rapidly. The Model Context Protocol (MCP) has emerged as a leading standard for giving AI applications real-world capabilities, while FastMCP provides a simple and developer-friendly way to build MCP servers that agents can discover and use.

In this FastMCP tutorial, we’ll explore how MCP works, why it has been widely adopted across the AI ecosystem, and how developers can rapidly build tools that integrate seamlessly with AI agents. Rather than focusing primarily on theory, this session will take a hands-on approach, guiding participants through the process of building a FastMCP server from scratch and integrating it into a LangGraph-powered agent.

We’ll begin with an introduction to MCP, its role in modern AI agent architectures, and how FastMCP simplifies server development through Python decorators, automatic schema generation, and built-in tooling. From there, we’ll build practical MCP tools live, test them using the MCP Inspector, and demonstrate how agents can interact with those tools through natural language.

The session will culminate in a complete end-to-end workflow where a LangGraph agent discovers and uses FastMCP tools to perform web searches, reason over retrieved information, and save results to a file – all triggered by a single user prompt.

What We Will Cover:

• Introduction to MCP and its role in modern AI agent architectures
• Why leading AI platforms and frameworks are adopting the Model Context Protocol
• Understanding FastMCP and its developer-friendly approach to MCP server development
• Building a FastMCP server from scratch using Python decorators
• Working with @mcp.tool, @mcp.resource, and auto-generated schemas
• Using the MCP Inspector to test and validate MCP tools
• Building practical tools including web search, URL fetching, note creation, and note retrieval
• Common design patterns for MCP-based tool development
• Connecting a FastMCP server to a LangGraph agent
• Tool discovery, agent reasoning loops, and handling tool responses
• Building an end-to-end AI agent workflow powered by MCP and LangGraph
• Best practices for creating reusable and extensible agent tooling

Hands-On Exercise:

Participants will follow this FastMCP tutorial live by building a FastMCP server from a blank project, creating and testing multiple MCP tools using the MCP Inspector. They will then connect the server to a LangGraph agent and execute an end-to-end workflow where the agent uses those tools to search for information, reason over results, and save outputs automatically.

In the final segment, attendees will extend the FastMCP server with a custom tool of their own, gaining practical experience with MCP development patterns and leaving with a working project that can be immediately adapted for real-world AI agent applications.

Who Should Attend:

• AI engineers and developers building agent-based applications
• Software engineers interested in MCP, LangGraph, and AI tooling
• Machine learning practitioners working with LLM-powered workflows
• Technical architects designing AI agent systems
• Developers looking to connect AI agents to external tools and data sources
• Anyone interested in modern AI agent frameworks and practical MCP implementation

Join us for a hands-on FastMCP tutorial and learn how to build powerful, tool-enabled AI agents using FastMCP and LangGraph—from a blank file to a fully functioning agent workflow.