FastMCP LangGraph Webinar Demo

<div align="center"> <h1>FastMCP + LangGraph Webinar Demo</h1> <p><b>A terminal-based MCP demo that connects a FastMCP server to a LangGraph ReAct agent.</b></p> </div>

<div align="center"> <img alt="Python" src="https://img.shields.io/badge/-Python-3776AB?style=for-the-badge&logo=python&logoColor=white" /> <img alt="FastMCP" src="https://img.shields.io/badge/-FastMCP-111111?style=for-the-badge&logo=fastapi&logoColor=white" /> <img alt="LangGraph" src="https://img.shields.io/badge/-LangGraph-0B1F33?style=for-the-badge&logo=langchain&logoColor=white" /> <img alt="OpenAI" src="https://img.shields.io/badge/-OpenAI-412991?style=for-the-badge&logo=openai&logoColor=white" /> <img alt="Tavily" src="https://img.shields.io/badge/-Tavily-1E88E5?style=for-the-badge&logo=google-chrome&logoColor=white" /> <img alt="httpx" src="https://img.shields.io/badge/-httpx-1C7ED6?style=for-the-badge&logo=python&logoColor=white" /> </div>

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What this project does

This repo is a clean CLI demo for MCP tool use.

• Starts a FastMCP server with 6 tools
• Connects the server to a LangGraph ReAct agent
• Lets you test tools manually from the terminal
• Lets the agent choose tools for multi-step tasks

Included files

• demo_mcp_server.py - MCP server with all tools
• demo_agent.py - interactive CLI runner
• requirements.txt - Python dependencies
• .env.example - local env template

Skills used

• Python
• FastMCP
• LangGraph
• MCP
• OpenAI API
• Tavily search API
• HTTP requests
• Agentic workflows
• Tool calling and tool selection
• Prompt engineering for ReAct-style agents
• Multi-step reasoning with external tools
• MCP server and client orchestration
• Web search and page fetching for grounding
• Safe task-specific automation from the terminal

Features

• web_search - Tavily-backed search
• fetch_url - fetches a URL and strips HTML
• save_note - writes text files under tmp/mcp_notes/
• read_note - reads saved notes back
• list_notes - lists saved notes and sizes
• calculate - safe math evaluation for expressions like sqrt(144) + pi

Requirements

• Python 3.10+
• OPENAI_API_KEY for the agent section
• TAVILY_API_KEY for web_search

Setup

pip install -r requirements.txt

Create a .env file in the project root with:

OPENAI_API_KEY=your_openai_key
OPENAI_MODEL=gpt-4o-mini
TAVILY_API_KEY=your_tavily_key

If you only want to test the tools and skip the agent, OPENAI_API_KEY is not required.

Run

python demo_agent.py

Tools-only mode:

python demo_agent.py --tools-only

How the demo works

1. The runner starts the MCP server over stdio.
2. It discovers the available tools.
3. You can call tools manually from the terminal.
4. If OPENAI_API_KEY is set, the LangGraph agent runs preset or custom prompts.

Environment variables

• OPENAI_API_KEY - required for the agent
• OPENAI_MODEL - optional, defaults to gpt-4o-mini
• TAVILY_API_KEY - enables web_search

Notes

• web_search fails cleanly if TAVILY_API_KEY is missing.
• Saved notes live in tmp/mcp_notes/ next to the scripts.
• save_note sanitizes filenames before writing.

Troubleshooting

• If imports fail, run pip install -r requirements.txt again.
• If the server file is missing, keep demo_agent.py and demo_mcp_server.py in the same folder.
• If the agent section is skipped, set OPENAI_API_KEY in .env.

License

No license file is included in this repo.