Leveraging MCP

Chat with your ZenML server

ZenML server supports a chat interface that allows you to interact with the server using natural language through the Model Context Protocol (MCP). This feature enables you to query your ML pipelines, analyze performance metrics, and generate reports using conversational language instead of traditional CLI commands or dashboard interfaces.

ZenML MCP Server Overview

What is MCP?

The Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to Large Language Models (LLMs). Think of it as a "USB-C port for AI applications" - providing a standardized way to connect AI models to different data sources and tools.

MCP follows a client-server architecture where:

• MCP Clients: Programs like Claude Desktop or IDEs (Cursor, Windsurf, etc.) that want to access data through MCP

• MCP Servers: Lightweight programs that expose specific capabilities through the standardized protocol. Our implementation is of an MCP server that connects to your ZenML server.

Why use MCP with ZenML?

The ZenML MCP Server offers several advantages for developers and teams:

1. Natural Language Interaction: Query your ZenML metadata, code and logs using conversational language instead of memorizing CLI commands or navigating dashboard interfaces.

2. Contextual Development: Get insights about failing pipelines or performance metrics without switching away from your development environment.

3. Accessible Analytics: Generate custom reports and visualizations about your pipelines directly through conversation.

4. Streamlined Workflows: Trigger pipeline runs via natural language requests when you're ready to execute.

You can get a sense of how it works in the following video:

Features

The ZenML MCP server provides access to core read functionality from your ZenML server, allowing you to get live information about:

• Users

• Stacks

• Pipelines

• Pipeline runs

• Pipeline steps

• Services

• Stack components

• Flavors

• Pipeline run templates

• Schedules

• Artifacts (metadata about data artifacts, not the data itself)

• Service Connectors

• Step code

• Step logs (if the step was run on a cloud-based stack)

It also allows you to trigger new pipeline runs through existing run templates.

Getting Started

For the most up-to-date setup instructions and code, please refer to the ZenML MCP Server GitHub repository. We recommend using the uv package manager to install the dependencies since it's the most reliable and fastest setup experience.

The setup process for the ZenML MCP Server is straightforward:‍

Prerequisites:

• Access to a ZenML Cloud server

• uv installed locally
• A local clone of the repository

Configuration:

• Create an MCP config file with your ZenML server details

• Configure your preferred MCP client (Claude Desktop or Cursor)

For detailed setup instructions, please refer to the GitHub repository.

Example Usage

Once set up, you can interact with your ZenML infrastructure through natural language. Here are some example prompts you can try:

1. Pipeline Analysis Report:

   Can you write me a report (as a markdown artifact) about the 'simple_pipeline' and tell the story of the history of its runs, which were successful etc., and what stacks worked, which didn't, as well as some performance metrics + recommendations?

Pipeline Analysis Report

1. Comparative Pipeline Analysis:

   Could you analyze all our ZenML pipelines and create a comparison report (as a markdown artifact) that highlights differences in success rates, average run times, and resource usage? Please include a section on which stacks perform best for each pipeline type.

Comparative Pipeline Analysis

1. Stack Component Analysis:

   Please generate a comprehensive report or dashboard on our ZenML stack components, showing which ones are most frequently used across our pipelines. Include information about version compatibility issues and performance variations.

Stack Component Analysis

Get Involved

We invite you to try the ZenML MCP Server and share your experiences with us through our Slack community. We're particularly interested in:

• Whether you need additional write actions (creating stacks, registering components, etc.)

• Examples of how you're using the server in your workflows

• Suggestions for additional features or improvements

Contributions and pull requests to the core repository are always welcome!