Math MCP Example: Server & Client

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Directory Structure & Environment Setup

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Recommended project structure:

iceberg-mcp-main/
├── iceberg_mcp/
│   └── math/
│       ├── math_server.py
│       ├── math_clinet.py
│       └── README.md
├── .venv/                # Python virtual environment (recommended)
└── ...                   # Other project files

Setting up your Python environment

1. Create a virtual environment (recommended):

   python3 -m venv .venv
   source .venv/bin/activate
   

2. Install required dependencies: (Make sure you are in the root directory or where your requirements are listed)

   pip install mcp fastmcp fastapi-mcp langchain-mcp-adapters uvicorn
   # And any other dependencies your project needs
   

3. Set your OpenAI API key:

   export OPENAI_API_KEY=sk-...your-key-here...
   

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Overview

• Server: Exposes math tools (add, sub, multiply) and prompt templates using FastMCP and SSE (Server-Sent Events).
• Client: Connects to the server, discovers available tools, and uses an LLM agent to invoke those tools.

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Learning Objectives

• Understand how to register and expose tools in a Python server.
• Learn how to connect to a tool server and discover available tools.
• See how an LLM agent can use external tools to answer questions.
• Practice async programming and client-server communication.

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Server: math_server.py

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What does it do?

• Registers three math tools: add, sub, multiply.
• Registers three prompt templates for natural language generation.
• Exposes an ASGI app for Uvicorn to serve via SSE.
• Logs every tool and prompt call for transparency.

Key code sections

from mcp.server.fastmcp import FastMCP
import logging

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("math_server")

# Create the server
mcp = FastMCP("Math")
app = mcp.sse_app  # Expose the SSE ASGI app

# Register tools
@mcp.tool()
def add(a: int, b: int) -> int:
    result = a + b
    logger.info(f"add({a}, {b}) = {result}")
    return result

# ... (sub, multiply, and prompts similar)

print("Registered tools:", mcp.list_tools())

>>>>>>>>>>>>>>>>>>>>>>>> How to run the server===================================================================================

From the iceberg_mcp/math directory:

uvicorn math_server:app --port 3000

Or from the project root:

uvicorn iceberg_mcp.math.math_server:app --port 3000

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Client: math_clinet.py

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What does it do?

• Connects to the math server using SSE.
• Discovers available tools.
• Uses a LangChain agent to ask the server to add 3 and 5.
• Prints only the final answer from the agent's response.

Key code sections

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
import asyncio

client = MultiServerMCPClient({
    "math": {
        "transport": "sse",
        "url": "http://localhost:3000/sse"
    },
})

async def main():
    tools = await client.get_tools()
    print("Discovered tools:", tools)
    agent = create_react_agent("openai:gpt-4.1", tools)
    math_response = await agent.ainvoke({"messages": "use the add tool to add 3 and 5"})
    if 'messages' in math_response and len(math_response['messages']) > 1:
        ai_message = math_response['messages'][-1]
        print(ai_message.content)
    else:
        print(math_response)

if __name__ == "__main__":
    asyncio.run(main())

OpenAI API Key Setup

To use the LLM agent (e.g., GPT-4), you need an OpenAI API key. This is required for the client to access OpenAI's language models.

How to set your OpenAI API key:

• The recommended way is to set the OPENAI_API_KEY environment variable in your shell:

export OPENAI_API_KEY=sk-...your-key-here...

• Alternatively, you can set it in your Python code (not recommended for production):

import os
os.environ["OPENAI_API_KEY"] = "sk-...your-key-here..."

You must set the API key before running the client, or you will get authentication errors.

>>>>>>>>>>>>>>>>>>> How to run the client################################################################

From the iceberg_mcp/math directory:

python math_clinet.py

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Experiment & Learn

• Try changing the numbers in the client prompt.
• Add new tools (e.g., division) to the server and see if the client discovers them.
• Add more prompts or logging to see how the server responds.
• Explore how async programming enables real-time tool discovery and invocation.

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Troubleshooting

• If the client prints [] for tools, check server logs and package versions.
• Make sure both server and client use compatible MCP and adapter versions.
• Ensure the server is running before starting the client.

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Summary

This example demonstrates how to:

• Build a tool-augmented AI server in Python
• Connect and interact with it using a modern LLM agent
• Use async programming for efficient, real-time communication

Happy learning! # mcp