What is Ai Tool Calling Agent With Langchain Mcp

AI-Tool-Calling-Agent-with-LangChain-MCP is an autonomous AI agent that utilizes LangChain’s ReAct framework and the Model Context Protocol (MCP) to reason and interact with external tools, such as performing math operations and providing weather forecasts.

Use cases

Use cases include answering user queries about weather conditions, performing calculations, and providing real-time data by calling external APIs through the agent.

How to use

To use the AI-Tool-Calling-Agent, simply input a question or command. The agent interprets the input, decides which tool to use, calls the appropriate external tool, and returns a final answer that combines its reasoning with the tool’s output.

Key features

Key features include a tool-calling agent powered by LangChain ReAct Agent, custom tools defined via MCP Server, asynchronous MCP client for fetching tool capabilities, integration with various tools like addition and weather forecasting, and operation on Groq LLaMA 3 70B Versatile.

Where to use

AI-Tool-Calling-Agent can be used in various fields such as customer support, educational tools, personal assistants, and any application requiring interaction with external data sources or tools.

AI-Tool-Calling-Agent-with-LangChain-MCP

🧠 AI Tool-Calling Agent with LangChain & MCP

An autonomous AI agent built using LangChain’s ReAct framework and Model Context Protocol (MCP). This project demonstrates how an LLM can reason and interact with external tools like math operations and weather forecasting through tool-calling.

🚀 Project Overview

This project showcases how to build an AI agent that can “think” and “act” by:

• Interpreting user questions

• Deciding what tool to use

• Calling external tools (like a calculator or weather app)

• Returning a final answer, combining LLM reasoning with tool outputs

🛠️ Features

✅ Tool-calling agent powered by LangChain ReAct Agent
✅ Custom tools defined and served via MCP Server
✅ Uses asynchronous MCP client to fetch tool capabilities
✅ Seamlessly integrates tools like add, multiply, and get_weather_forecast(city)
✅ Runs on Groq LLaMA 3 70B Versatile via langchain-groq

📦 Tech Stack

🧪 Example Prompt

What’s the current weather condition in Chennai?

Behind the scenes:
The LLM receives the prompt.

It decides to call the get_weather_forecast(city) tool.

Tool gets invoked from the MCP Server.

Result is returned and combined into a natural response by the agent.

How It Works

🔨 Step 1: Define MCP Tools

math_server.py

@math_mcp.tool()
def add(a: int, b: int) -> int:
return a + b

@math_mcp.tool()
def multiply(a: int, b: int) -> int:
return a * b

🔗 Step 2: Connect AI Agent to MCP Server

async with stdio_client(server_parameters) as (read, write):

async with ClientSession(read, write) as session:
    await session.initialize()
    mcp_tools = await load_mcp_tools(session)
    ai_agent = create_react_agent(llm_model, mcp_tools)
    response = await ai_agent.ainvoke({"messages": prompt})

🧭 Running the Project

Start MCP Server:

python mcp_server.py

Run the AI Agent:

python run_agent.py

🧰 Tool Debugging with MCP Inspector (Optional)

Install and run the visual MCP Inspector:

npx mcp inspector

This launches a local web app where one can test the tool interface visually.

✔ Learnings and Key takeaways from Implementing this Simple Agent:

• Understanding of LangChain’s ReAct Agent architecture

• Tool-Calling design using Model Context Protocol (MCP)

• Practical use of async programming and agent-to-tool messaging

• Combining LLMs + Tools to create semi-autonomous AI systems

📌 Credits

LangChain

Model Context Protocol

Groq - blazing fast inference

LangChain MCP Adapters