Utilities for creating and debugging MCP tools
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# EZ MCP Toolbox *A Comet ML Open Source Project* This Python toolbox contains four 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](https://www.comet.com/site/products/opik/) 3. `ez-mcp-eval` - evaluate LLM applications using Opik's evaluation framework 4. `ez-mcp-optimize` - optimize LLM applications using Opik's optimization framework ## Why? 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](https://github.com/comet-ml/opik). Although the [Opik Playground](https://www.comet.com/docs/opik/opik-university/prompt-engineering/prompt-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. The `ez-mcp-eval` and `ez-mcp-optimize` commands provide evaluation and optimization capabilities for your LLM applications, enabling you to measure performance and automatically improve your prompts using Opik's evaluation and optimization frameworks. ## Installation ``` pip install ez-mcp-toolbox --upgrade ``` ## Quick start ### Interactive Chat with MCP Tools ``` ez-mcp-chatbot ``` That will start a `ez-mcp-server` (using example tools below) and the `ez-mcp-chatbot` configured to use those tools. ### Evaluate LLM Applications ``` ez-mcp-eval --prompt "Answer the question" --dataset "my-dataset" --metric "Hallucination" --output "reference=answer" ``` This will evaluate your LLM application using Opik's evaluation framework with your dataset and chosen metrics. The `--output` parameter is required for cla…
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