A Comet ML Open Source Project

This Python toolbox contains two command-line easy to use utilities:

1. ez-mcp-server - turns a file of Python functions into a MCP server
2. ez-mcp-chatbot - interactively debug MCP servers, with traces logged to Opik

The ez-mcp-server allows a quick way to examine tools, signatures, descriptions, latency, and return values. Combined with the chatbot, you can create a fast workflow to interate on your MCP tools.

The ez-mcp-chatbot allows a quick method to examine and debug LLM and MCP tool interactions, with observability available through Opik. Although the Opik Playground gives you the ability to test your prompts on datasets, do A/B testing, and more, this chatbot gives you a command-line interaction, debugging tools, combined with Opik observability.

pip install ez-mcp-toolbox --upgrade 

ez-mcp-chatbot 

That will start a ez-mcp-server (using example tools below) and the ez-mcp-chatbot configured to use those tools.

You can customize the chatbot's behavior with a custom system prompt:

# Use a custom system prompt ez-mcp-chatbot --system-prompt "You are a helpful coding assistant" # Create a default configuration ez-mcp-chatbot --init

Example dialog:

This interaction of the LLM with the MCP tools will be logged, and available for examination and debugging in Opik:

The rest of this file describes these two commands.

A command-line utility for turning a regular file of Python functions or classes into a full-fledged MCP server.

Take an existing Python file of functions, such as this file, my_tools.py:

# my_tools.py def add_numbers(a: float, b: float) -> float: """  Add two numbers together.    Args:  a: First number to add  b: Second number to add    Returns:  The sum of a and b  """ return a + b def greet_user(name: str) -> str: """  Greet a user with a welcoming message.    Args:  name: The name of the person to greet    Returns:  A personalized greeting message  """ return f"Welcome to ez-mcp-server, {name}!"

Then run the server with your custom tools:

ez-mcp-server my_tools.py

The server will automatically:

• Load all functions from your file (no ez_mcp_toolbox imports required)
• Convert them to MCP tools
• Generate JSON schemas from your function signatures
• Use your docstrings as tool descriptions

Note: if you just launch the server, it will wait for stdio input. This is designed to run from inside a system that will dynamically start the server (see below).

ez-mcp-server [-h] [--transport {stdio,sse}] [--host HOST] [--port PORT] [tools_file] 

Positional arguments:

• tools_file - Path to the tools file containing functions to serve as MCP tools (default: tools.py)

Options:

• -h, --help - show this help message and exit
• --transport {stdio,sse} - Transport method to use (default: stdio)
• --host HOST - Host for SSE transport (default: localhost)
• --port PORT - Port for SSE transport (default: 8000)

A powerful AI chatbot that integrates with Model Context Protocol (MCP) servers and provides observability through Opik tracing. This chatbot can connect to various MCP servers to access specialized tools and capabilities, making it a versatile assistant for different tasks.

• MCP Integration: Connect to multiple Model Context Protocol servers for specialized tool access
• Opik Observability: Built-in tracing and observability with Opik integration
• Interactive Chat Interface: Rich console interface with command history and auto-completion
• Python Code Execution: Execute Python code directly in the chat environment
• Tool Management: Discover and use tools from connected MCP servers
• Configurable: JSON-based configuration for models and MCP servers
• Async Support: Full asynchronous operation for better performance

The server implements the full MCP specification:

• Tool Discovery: Dynamic tool listing and metadata
• Tool Execution: Asynchronous tool calling with proper error handling
• Protocol Compliance: Full compatibility with MCP clients
• Extensibility: Easy addition of new tools and capabilities

Create a default configuration file:

ez-mcp-chatbot --init

This creates a config.json file with default settings.

Edit config.json to specify your model and MCP servers. For example:

{ "model": "openai/gpt-4o-mini", "model_kwargs": { "temperature": 0.2 }, "mcp_servers": [ { "name": "ez-mcp-server", "description": "Ez MCP server from Python files", "command": "ez-mcp-server", "args": ["/path/to/my_tools.py"] } ] }

Supported model formats:

• openai/gpt-4o-mini
• anthropic/claude-3-sonnet
• google/gemini-pro
• And many more through LiteLLM

Inside the ez-mcp-chatbot, you can have a normal LLM conversation.

In addition, you have access to the following meta-commands:

• /clear - Clear the conversation history
• /help - Show available commands
• /debug on or /debug off to toggle debug output
• /show tools - to list all available tools
• /show tools SERVER - to list tools for a specific server
• /run SERVER.TOOL - to execute a tool
• ! python_code - to execute Python code (e.g., '! print(2+2)')
• quit or exit - Exit the chatbot

Execute Python code by prefixing with !:

! print(self.messages) ! import math ! math.sqrt(16) 

The chatbot automatically discovers and uses tools from connected MCP servers. Simply ask questions that require tool usage, and the chatbot will automatically call the appropriate tools.

The chatbot uses a system prompt to define its behavior and personality. You can customize this using the --system-prompt command line option.

By default, the chatbot uses this system prompt:

You are a helpful AI system for answering questions that can be answered with any of the available tools. 

You can override the default system prompt to customize the chatbot's behavior:

# Make it a coding assistant ez-mcp-chatbot --system-prompt "You are an expert Python developer who helps with coding tasks." # Make it a data analyst ez-mcp-chatbot --system-prompt "You are a data scientist who specializes in analyzing datasets and creating visualizations." # Make it more conversational ez-mcp-chatbot --system-prompt "You are a friendly AI assistant who loves to help users with their questions and tasks."

The system prompt affects how the chatbot:

• Interprets user requests
• Decides which tools to use
• Structures its responses
• Maintains conversation context

The chatbot includes built-in Opik observability integration:

For the command-line flag --opik:

• hosted (default): Use hosted Opik service
• local: Use local Opik instance
• disabled: Disable Opik tracing

Set environment variables for Opik:

# For hosted mode export OPIK_API_KEY=your_opik_api_key # For local mode export OPIK_LOCAL_URL=http://localhost:8080

# Use hosted Opik (default) ez-mcp-chatbot --opik hosted # Use local Opik ez-mcp-chatbot --opik local # Disable Opik ez-mcp-chatbot --opik disabled # Use custom system prompt ez-mcp-chatbot --system-prompt "You are a helpful coding assistant" # Combine options ez-mcp-chatbot --system-prompt "You are a data analysis expert" --opik local --debug

• --opik {local,hosted,disabled} - Opik tracing mode (default: hosted)
• --system-prompt TEXT - Custom system prompt for the chatbot (overrides default)
• --debug - Enable debug output during processing
• --init - Create a default config.json file and exit
• config_path - Path to the configuration file (default: config.json)

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

• Documentation: GitHub Repository
• Issues: GitHub Issues

• Built with Model Context Protocol (MCP)
• Powered by LiteLLM
• Observability by Opik
• Rich console interface by Rich

1. Fork the repository
2. Create a feature branch: git checkout -b feature-name
3. Make your changes
4. Run tests: pytest
5. Format code: black . && isort .
6. Commit your changes: git commit -m "Add feature"
7. Push to the branch: git push origin feature-name
8. Submit a pull request

• Python 3.8 or higher
• OpenAI, Anthropic, or other LLM provider API key (for chatbot functionality)

# Clone the repository git clone https://github.com/comet-ml/ez-mcp-toolbox.git cd ez-mcp-toolbox # Install in development mode pip install -e . # Or install with development dependencies pip install -e ".[dev]"

pip install -r requirements.txt