LinkedIn Browser MCP Server

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For Client Developers
Get started building your own client that can integrate with all MCP servers.

In this tutorial, you’ll learn how to build a LLM-powered chatbot client that connects to MCP servers. It helps to have gone through the Server quickstart that guides you through the basic of building your first server.

Python
Java
You can find the complete code for this tutorial here.

System Requirements
Before starting, ensure your system meets these requirements:

Mac or Windows computer
Latest Python version installed
Latest version of uv installed
Setting Up Your Environment
First, create a new Python project with uv:


# Create project directory
uv init mcp-client
cd mcp-client

# Create virtual environment
uv venv

# Activate virtual environment
# On Windows:
.venv\Scripts\activate
# On Unix or MacOS:
source .venv/bin/activate

# Install required packages
uv add mcp anthropic python-dotenv

# Remove boilerplate files
rm hello.py

# Create our main file
touch client.py
Setting Up Your API Key
You’ll need an Anthropic API key from the Anthropic Console.

Create a .env file to store it:


# Create .env file
touch .env
Add your key to the .env file:


ANTHROPIC_API_KEY=<your key here>
Add .env to your .gitignore:


echo ".env" >> .gitignore
Make sure you keep your ANTHROPIC_API_KEY secure!

Creating the Client
Basic Client Structure
First, let’s set up our imports and create the basic client class:


import asyncio
from typing import Optional
from contextlib import AsyncExitStack

from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

from anthropic import Anthropic
from dotenv import load_dotenv

load_dotenv()  # load environment variables from .env

class MCPClient:
    def __init__(self):
        # Initialize session and client objects
        self.session: Optional[ClientSession] = None
        self.exit_stack = AsyncExitStack()
        self.anthropic = Anthropic()
    # methods will go here
Server Connection Management
Next, we’ll implement the method to connect to an MCP server:


async def connect_to_server(self, server_script_path: str):
    """Connect to an MCP server

    Args:
        server_script_path: Path to the server script (.py or .js)
    """
    is_python = server_script_path.endswith('.py')
    is_js = server_script_path.endswith('.js')
    if not (is_python or is_js):
        raise ValueError("Server script must be a .py or .js file")

    command = "python" if is_python else "node"
    server_params = StdioServerParameters(
        command=command,
        args=[server_script_path],
        env=None
    )

    stdio_transport = await self.exit_stack.enter_async_context(stdio_client(server_params))
    self.stdio, self.write = stdio_transport
    self.session = await self.exit_stack.enter_async_context(ClientSession(self.stdio, self.write))

    await self.session.initialize()

    # List available tools
    response = await self.session.list_tools()
    tools = response.tools
    print("\nConnected to server with tools:", [tool.name for tool in tools])
Query Processing Logic
Now let’s add the core functionality for processing queries and handling tool calls:


async def process_query(self, query: str) -> str:
    """Process a query using Claude and available tools"""
    messages = [
        {
            "role": "user",
            "content": query
        }
    ]

    response = await self.session.list_tools()
    available_tools = [{
        "name": tool.name,
        "description": tool.description,
        "input_schema": tool.inputSchema
    } for tool in response.tools]

    # Initial Claude API call
    response = self.anthropic.messages.create(
        model="claude-3-5-sonnet-20241022",
        max_tokens=1000,
        messages=messages,
        tools=available_tools
    )

    # Process response and handle tool calls
    tool_results = []
    final_text = []

    assistant_message_content = []
    for content in response.content:
        if content.type == 'text':
            final_text.append(content.text)
            assistant_message_content.append(content)
        elif content.type == 'tool_use':
            tool_name = content.name
            tool_args = content.input

            # Execute tool call
            result = await self.session.call_tool(tool_name, tool_args)
            tool_results.append({"call": tool_name, "result": result})
            final_text.append(f"[Calling tool {tool_name} with args {tool_args}]")

            assistant_message_content.append(content)
            messages.append({
                "role": "assistant",
                "content": assistant_message_content
            })
            messages.append({
                "role": "user",
                "content": [
                    {
                        "type": "tool_result",
                        "tool_use_id": content.id,
                        "content": result.content
                    }
                ]
            })

            # Get next response from Claude
            response = self.anthropic.messages.create(
                model="claude-3-5-sonnet-20241022",
                max_tokens=1000,
                messages=messages,
                tools=available_tools
            )

            final_text.append(response.content[0].text)

    return "\n".join(final_text)
Interactive Chat Interface
Now we’ll add the chat loop and cleanup functionality:


async def chat_loop(self):
    """Run an interactive chat loop"""
    print("\nMCP Client Started!")
    print("Type your queries or 'quit' to exit.")

    while True:
        try:
            query = input("\nQuery: ").strip()

            if query.lower() == 'quit':
                break

            response = await self.process_query(query)
            print("\n" + response)

        except Exception as e:
            print(f"\nError: {str(e)}")

async def cleanup(self):
    """Clean up resources"""
    await self.exit_stack.aclose()
Main Entry Point
Finally, we’ll add the main execution logic:


async def main():
    if len(sys.argv) < 2:
        print("Usage: python client.py <path_to_server_script>")
        sys.exit(1)

    client = MCPClient()
    try:
        await client.connect_to_server(sys.argv[1])
        await client.chat_loop()
    finally:
        await client.cleanup()

if __name__ == "__main__":
    import sys
    asyncio.run(main())
You can find the complete client.py file here.

Key Components Explained
1. Client Initialization
The MCPClient class initializes with session management and API clients
Uses AsyncExitStack for proper resource management
Configures the Anthropic client for Claude interactions
2. Server Connection
Supports both Python and Node.js servers
Validates server script type
Sets up proper communication channels
Initializes the session and lists available tools
3. Query Processing
Maintains conversation context
Handles Claude’s responses and tool calls
Manages the message flow between Claude and tools
Combines results into a coherent response
4. Interactive Interface
Provides a simple command-line interface
Handles user input and displays responses
Includes basic error handling
Allows graceful exit
5. Resource Management
Proper cleanup of resources
Error handling for connection issues
Graceful shutdown procedures
Common Customization Points
Tool Handling

Modify process_query() to handle specific tool types
Add custom error handling for tool calls
Implement tool-specific response formatting
Response Processing

Customize how tool results are formatted
Add response filtering or transformation
Implement custom logging
User Interface

Add a GUI or web interface
Implement rich console output
Add command history or auto-completion
Running the Client
To run your client with any MCP server:


uv run client.py path/to/server.py # python server
uv run client.py path/to/build/index.js # node server
If you’re continuing the weather tutorial from the server quickstart, your command might look something like this: python client.py .../weather/src/weather/server.py

The client will:

Connect to the specified server
List available tools
Start an interactive chat session where you can:
Enter queries
See tool executions
Get responses from Claude
Here’s an example of what it should look like if connected to the weather server from the server quickstart:


How It Works
When you submit a query:

The client gets the list of available tools from the server
Your query is sent to Claude along with tool descriptions
Claude decides which tools (if any) to use
The client executes any requested tool calls through the server
Results are sent back to Claude
Claude provides a natural language response
The response is displayed to you
Best practices
Error Handling

Always wrap tool calls in try-catch blocks
Provide meaningful error messages
Gracefully handle connection issues
Resource Management

Use AsyncExitStack for proper cleanup
Close connections when done
Handle server disconnections
Security

Store API keys securely in .env
Validate server responses
Be cautious with tool permissions
Troubleshooting
Server Path Issues
Double-check the path to your server script is correct
Use the absolute path if the relative path isn’t working
For Windows users, make sure to use forward slashes (/) or escaped backslashes (\) in the path
Verify the server file has the correct extension (.py for Python or .js for Node.js)
Example of correct path usage:


# Relative path
uv run client.py ./server/weather.py

# Absolute path
uv run client.py /Users/username/projects/mcp-server/weather.py

# Windows path (either format works)
uv run client.py C:/projects/mcp-server/weather.py
uv run client.py C:\\projects\\mcp-server\\weather.py
Response Timing
The first response might take up to 30 seconds to return
This is normal and happens while:
The server initializes
Claude processes the query
Tools are being executed
Subsequent responses are typically faster
Don’t interrupt the process during this initial waiting period
Common Error Messages
If you see:

FileNotFoundError: Check your server path
Connection refused: Ensure the server is running and the path is correct
Tool execution failed: Verify the tool’s required environment variables are set
Timeout error: Consider increasing the timeout in your client configuration