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

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:

Setting Up Your API Key

You’ll need an Anthropic API key from the Anthropic Console.

Create a .env file to store it:

Add your key to the .env file:

Add .env to your .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:

Server Connection Management

Next, we’ll implement the method to connect to an MCP server:

Query Processing Logic

Now let’s add the core functionality for processing queries and handling tool calls:

Interactive Chat Interface

Now we’ll add the chat loop and cleanup functionality:

Main Entry Point

Finally, we’ll add the main execution logic:

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:

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:

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

Python#

System Requirements#

Setting Up Your Environment#

Setting Up Your API Key#

Creating the Client#

Basic Client Structure#

Server Connection Management#

Query Processing Logic#

Interactive Chat Interface#

Main Entry Point#

Key Components Explained#

1. Client Initialization#

2. Server Connection#

3. Query Processing#

4. Interactive Interface#

5. Resource Management#

Common Customization Points#

Running the Client#

How It Works#

Best practices#

Troubleshooting#

Server Path Issues#

Response Timing#

Common Error Messages#