Extend Click applications with Model Context Protocol (MCP) support
Project description
click-mcp
A Python library that extends Click applications with Model Context Protocol (MCP) support, allowing AI agents to interact with CLI tools.
Overview
click-mcp provides a simple decorator that converts Click commands into MCP tools. This enables AI agents to discover and interact with your CLI applications programmatically.
The Model Context Protocol (MCP) is an open standard for AI agents to interact with tools and applications in a structured way.
Key Features
- Simple
@click_mcpdecorator syntax - Automatic conversion of Click commands to MCP tools
- Support for nested command groups
- Stdio-based MCP server for easy integration
Installation
pip install click-mcp
Basic Usage
import click
from click_mcp import click_mcp
@click_mcp(server_name="my-cli-app")
@click.group()
def cli():
"""Sample CLI application."""
pass
@cli.command()
@click.option('--name', required=True, help='Name to greet')
def greet(name):
"""Greet someone."""
click.echo(f"Hello, {name}!")
if __name__ == '__main__':
cli()
When you run the MCP server, Click commands are converted into MCP tools:
- Command
greetbecomes MCP toolgreet - Nested commands use dot notation (e.g.,
users.create)
To invoke a command via MCP, send a request like:
{
"type": "invoke",
"tool": "greet",
"parameters": {
"name": "World"
}
}
To start the MCP server:
$ python my_app.py mcp
Advanced Usage
Customizing the MCP Command Name
By default, click-mcp adds an mcp command to your CLI application. You can customize this name using the command_name parameter:
@click_mcp(command_name="start-mcp")
@click.group()
def cli():
"""Sample CLI application with custom MCP command name."""
pass
With this configuration, you would start the MCP server using:
$ python my_app.py start-mcp
This can be useful when:
- The name "mcp" conflicts with an existing command
- You want a more descriptive command name
- You're integrating with a specific AI agent that expects a certain command name
Customizing the MCP Server Name
You can also customize the name of the MCP server that's reported to clients:
@click_mcp(server_name="my-custom-tool")
@click.group()
def cli():
"""Sample CLI application with custom server name."""
pass
This can be useful when:
- You want to provide a more descriptive name for your tool
- You're integrating with systems that use the server name for identification
- You want to distinguish between different MCP-enabled applications
Working with Nested Command Groups
click-mcp supports nested command groups. When you have a complex CLI structure with subcommands, all commands are exposed as MCP tools:
@click_mcp
@click.group()
def cli():
"""Main CLI application."""
pass
@cli.group()
def users():
"""User management commands."""
pass
@users.command()
@click.option('--username', required=True)
def create(username):
"""Create a new user."""
click.echo(f"Creating user: {username}")
@users.command()
@click.argument('username')
def delete(username):
"""Delete a user."""
click.echo(f"Deleting user: {username}")
When exposed as MCP tools, the nested commands will be available with their full path using dot notation (e.g., "users.create" and "users.delete").
Handling Command Errors
When a Click command raises an exception, click-mcp captures the error and returns it as part of the MCP response. This allows AI agents to handle errors gracefully:
@cli.command()
@click.option('--filename', required=True)
def process(filename):
"""Process a file."""
try:
with open(filename, 'r') as f:
content = f.read()
click.echo(f"Processed file: {filename}")
except FileNotFoundError:
raise click.UsageError(f"File not found: {filename}")
If the file doesn't exist, the AI agent will receive an error message that it can present to the user or use to take corrective action.
Development
Setup
Clone the repository and install Hatch:
git clone https://github.com/aws/click-mcp.git
cd click-mcp
pip install hatch
Testing
Run tests with Hatch:
# Run all tests
hatch run test
# Run tests with coverage
hatch run cov
Code Formatting
Format code with Black:
# Format code
hatch run format
# Check formatting
hatch run check-format
Linting
Run linting checks with Ruff:
hatch run lint
Type Checking
Run type checking with MyPy:
hatch run typecheck
Run All Checks
Run all checks (formatting, linting, type checking, and tests):
hatch run check-all
Building
Build the package:
hatch run build
Documentation
Generate documentation:
hatch run docs
Related Resources
- Model Context Protocol (MCP) Specification
- Click Documentation
- MCP Tools Registry - A collection of MCP-compatible tools
License
MIT
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