Master the Art of Building an MCP Client in 3 simple steps

This guide walks you through creating a smart agent that interacts with an MCP (Model Context Protocol) server using the mcp-use library. Leveraging LangChain’s ReAct-style architecture, you’ll be able to design agents that can reason, use tools, and complete complex tasks step-by-step.

This is the third installment in the MCP series. If you haven’t yet, check out:
Build an MCP Server with Python
What is MCP in AI: The Building Blocks of AI

Overview

This example shows how to Build an MCP Client using:

LangChain’s ChatOpenAI model
- LangChain helps manage prompts, memory, and reasoning in a structured way.
- It’s great for building intelligent, multi-step agents quickly.
MCPClient and MCPAgent from the mcp-use library
- These tools handle communication with an MCP (Model Context Protocol) server.
- They help structure agent workflows and manage context.

How MCP Agents Behave

When you build an MCP client, it’s important to understand how MCP agents work. They follow a simple but powerful pattern called ReAct — short for Reasoning and Acting.

Here’s how it works:

Think Before Acting: The agent breaks down each task into small steps. First, it reasons about the problem — asking, “What should I do next?”
Use Tools via MCP: It uses tools that are available through the connected MCP server. These tools can be anything from a calculator to a document search function — whatever is exposed by your Model Context Protocol setup.
Observe and Adjust: After using a tool, the agent looks at the output (called an “observation”) and uses that result to decide what to do next.
Know When to Stop: The agent keeps repeating this think-act-observe cycle until it either finishes the task or hits a set limit of steps (max_steps).

Requirements

Before you start building your MCP (Model Context Protocol) client, make sure you have the following set up:

1. Install Required Packages

Use pip to install the core libraries you’ll need:

pip install mcp-use langchain-openai

pip install mcp-use langchain-openai

These libraries help your MCP client communicate with the Model Context Protocol and OpenAI’s language models.

2. GitHub Repository

You can find the source code and examples on GitHub:
mcp-use/mcp-use

3. OpenAI API Key

Make sure you have a valid OpenAI API key.
Set it as an environment variable:

export OPENAI_API_KEY=your-api-key-here

export OPENAI_API_KEY=your-api-key-here

This allows your MCP client to securely connect to OpenAI’s services.

4. Access to an MCP Server

You’ll need access to a running MCP server either:

Locally (for testing or development), or
Remotely (a hosted or cloud-based MCP instance)

Code Example

Let’s walk through a simple example of how to build an MCP client using langchain-openai and mcp-use.

Create a Python File

Create a new file named client.py and add the following code:

import asyncio
from langchain_openai import ChatOpenAI
from mcp_use import MCPClient, MCPAgent

async def main():
    # Connect to the MCP server
    client = MCPClient(config={
        "mcpServers": {
            "default": {
                "url": "https://your-mcp-server-url.com/mcp"
            }
        }
    })

    # Set up your language model (using OpenAI)
    llm = ChatOpenAI(model="gpt-4.1")

    # Initialize the MCP Agent with your LLM and client
    agent = MCPAgent(llm=llm, client=client, max_steps=10)

    # Run a task
    resp = await agent.run(
        "List all the books in the database and search for each on the internet, then gather their descriptions and metadata."
    )
    print(resp)

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

import asyncio
from langchain_openai import ChatOpenAI
from mcp_use import MCPClient, MCPAgent

async def main():
    # Connect to the MCP server
    client = MCPClient(config={
        "mcpServers": {
            "default": {
                "url": "https://your-mcp-server-url.com/mcp"
            }
        }
    })

    # Set up your language model (using OpenAI)
    llm = ChatOpenAI(model="gpt-4.1")

    # Initialize the MCP Agent with your LLM and client
    agent = MCPAgent(llm=llm, client=client, max_steps=10)

    # Run a task
    resp = await agent.run(
        "List all the books in the database and search for each on the internet, then gather their descriptions and metadata."
    )
    print(resp)

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

Connecting to MCP with Different Configuration Options

You can connect to an MCP server in multiple ways, depending on your use case. Here’s how to configure it:

1. Remote MCP Server (Hosted)

config = {
    "mcpServers": {
        "remote": {
            "url": "https://your-remote-mcp-url.com/mcp"
        }
    }
}

config = {
    "mcpServers": {
        "remote": {
            "url": "https://your-remote-mcp-url.com/mcp"
        }
    }
}

Use this if you’re connecting to a cloud-hosted MCP server.

2. Local MCP Server

config = {
    "mcpServers": {
        "local": {
            "url": "http://localhost:8080/mcp/sse"
        }
    }
}

config = {
    "mcpServers": {
        "local": {
            "url": "http://localhost:8080/mcp/sse"
        }
    }
}

Use this when running an MCP server on your local machine, e.g., during development or testing.

Start MCP from a CLI Command

For more dynamic use cases (like running the MCP server with a custom command), you can configure it like this:

config = {
    "mcpServers": {
        "cli": {
            "command": "uv",
            "args": ["run", "--with", "mcp", "mcp", "run", "server.py"]
        }
    }
}

config = {
    "mcpServers": {
        "cli": {
            "command": "uv",
            "args": ["run", "--with", "mcp", "mcp", "run", "server.py"]
        }
    }
}

This allows you to run an MCP server directly from a CLI process on startup.

Sample Output – Execution Trace

Here’s what a typical interaction looks like when using your MCP client to process a natural language query:

User Input:

Search books in my database and tell me the authors, topics, and some details about each of them.

Behind the Scenes (Agent Trace)

🚀 Initializing MCP agent and connecting to services...
🧠 Agent ready with tools: fetch_books, internet_search, summarize_web_content...
💬 Received query...
🏁 Starting agent execution with max_steps=10

👣 Step 1/10
🔧 Tool call: fetch_books → ✅ Found 4 books

👣 Step 2/10
🔧 Tool call: duckduckgo_detailed_search → Searched each book

👣 Step 3/10
🔧 Tool call: summarize_web_content → Combined descriptions + metadata

✅ Agent finished at step 3
🎉 Execution complete in 23.1 seconds

🚀 Initializing MCP agent and connecting to services...
🧠 Agent ready with tools: fetch_books, internet_search, summarize_web_content...
💬 Received query...
🏁 Starting agent execution with max_steps=10

👣 Step 1/10
🔧 Tool call: fetch_books → ✅ Found 4 books

👣 Step 2/10
🔧 Tool call: duckduckgo_detailed_search → Searched each book

👣 Step 3/10
🔧 Tool call: summarize_web_content → Combined descriptions + metadata

✅ Agent finished at step 3
🎉 Execution complete in 23.1 seconds

Sample Output – Final Response

Here’s the clean and user-friendly output that the MCP client generates:

1. **Chess Strategy, by Edward Lasker**
   - Description: Classic chess guide from 1921
   - Topics: Chess, Strategy
   - Links: Archive.org, Project Gutenberg

2. **Deep Learning with Python**
   - Author: François Chollet
   - Topics: Deep Learning, Python, Keras
   - Links: Manning, O'Reilly, IEEE Xplore
...

1. **Chess Strategy, by Edward Lasker**
   - Description: Classic chess guide from 1921
   - Topics: Chess, Strategy
   - Links: Archive.org, Project Gutenberg

2. **Deep Learning with Python**
   - Author: François Chollet
   - Topics: Deep Learning, Python, Keras
   - Links: Manning, O'Reilly, IEEE Xplore
...

With just one prompt, the MCP client fetches structured, enriched data by coordinating multiple tools—showing how Model Context Protocol makes AI interactions more powerful and traceable.

Key Concepts

Concept	Description
`MCPClient`	Interface to connect with the MCP server
`MCPAgent`	LangChain-compatible agent for reasoning + tool usage
`max_steps`	Max number of reasoning-act cycles per task
Tool Chaining	Agent calls multiple tools in sequence
Async Execution	All MCP calls are fully asynchronous

Summary: You’ve Built Your First MCP Client

By now, you’ve successfully set up and run a working example of an MCP (Model Context Protocol) client. Here’s what you’ve accomplished:

Connected to an MCP server (local or remote)
Launched a LangChain-based agent using OpenAI’s models
Executed complex, multi-step tasks with smart tool integration

This gives you a solid foundation to build powerful, intelligent AI systems that can use tools, make decisions, and scale in real-world applications.

From here, you’re ready to take the next steps toward production-ready, tool-augmented AI.

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