FastMCP + LangGraph Webinar Demo

A live-coding demo for the "AI Agents Are Only As Useful As the Tools They Can Reach" webinar.
Builds a real MCP server with six tools, connects it to a LangGraph ReAct agent, and lets you pick exactly which tool to call and what question to ask — all from the terminal.

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What's in this repo

File	Purpose
demo_mcp_server.py	The MCP server — defines all six tools
demo_agent.py	The interactive demo runner
requirements.txt	Python dependencies
.env.example	Template for your API keys

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Quick start

1. Clone / download the files

Make sure demo_mcp_server.py and demo_agent.py are in the same folder.

2. Install dependencies

pip install -r requirements.txt

Python 3.10+ required.

3. Set up your API keys

cp .env.example .env

Open .env and fill in your keys (see API keys below).

4. Run the demo

python demo_agent.py

No OpenAI key yet? Run in tools-only mode — you can still test all six tools manually:

python demo_agent.py --tools-only

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API keys

OpenAI (required for the agent — Section 3)

1. Go to https://platform.openai.com/api-keys
2. Click Create new secret key
3. Copy the key and paste it as OPENAI_API_KEY in your .env

The demo uses gpt-4o-mini by default — the cheapest model that handles tool-calling well.
Change it by setting OPENAI_MODEL=gpt-4o in .env if you want the more powerful version.

Cost note: A full run-through of the demo costs roughly $0.01–0.05 with gpt-4o-mini.

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Tavily (required for web_search tool)

1. Go to https://app.tavily.com and sign up (free)
2. Copy your API key from the dashboard
3. Paste it as TAVILY_API_KEY in your .env

Free tier: 1,000 searches/month — more than enough for demos.

Without this key the web_search tool returns an error message, but all other tools work fine.

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Demo walkthrough

Section 1 — Tool Discovery

Automatically connects to the MCP server and lists all available tools with their descriptions.

Section 2 — Interactive Tool Testing

You choose which tool to call and supply its arguments yourself. No LLM involved — raw tool input/output.

Available tools:
  1. web_search
  2. fetch_url
  3. save_note
  4. read_note
  5. list_notes
  6. calculate

Enter tool name or number (or 'done'): 6
  Tool : calculate
  Args : ['expression']
    expression (string): sqrt(144) + pi

Type done when you're ready to move on.

Section 3 — LangGraph Agent Demo

The agent picks its own tools based on your question. You can choose from preset questions or type your own.

Preset questions:
  1. Single tool — calculator
  2. Multi-tool — search then save
  3. Full workflow — search, fetch, calculate, save
  4. Read back a saved file
  5. Custom question

> 5
   Type your question: What is 2 to the power of 32?

• Type quiet to toggle the verbose tool-call trace on/off
• Type done to end this section

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The six tools

Tool	Description	Requires
web_search	Real web search via Tavily	TAVILY_API_KEY
fetch_url	Fetches and strips HTML from any URL	—
calculate	Evaluates math expressions safely (sqrt, pi, log, etc.)	—
save_note	Writes text to /tmp/mcp_notes/<filename>	—
read_note	Reads a previously saved note	—
list_notes	Lists all saved notes with sizes	—

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Troubleshooting

ModuleNotFoundError
Run pip install -r requirements.txt again. If you're in a virtual environment, make sure it's activated.

OPENAI_API_KEY not set
Make sure you copied .env.example to .env (not .env.example) and filled in the key.

Server file not found
demo_agent.py and demo_mcp_server.py must be in the same directory.

web_search returns an error
Add your TAVILY_API_KEY to .env. All other tools still work without it.

Agent gives a wrong answer / tool call fails
Try adding more detail to your question. The agent uses tool descriptions to decide what to call — more specific questions get better results.

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Resources

• FastMCP docs
• LangGraph docs
• langchain-mcp-adapters
• Model Context Protocol spec
• Tavily API
• OpenAI API keys