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

The easiest way to set up the ZenML MCP Server is through the MCP Settings page in the ZenML dashboard. This provides a guided experience for configuring your IDE or AI assistant to connect to your ZenML server.

Using the Dashboard Settings Page (Recommended)

Both ZenML OSS and ZenML Pro include an MCP settings page that generates the correct configuration for your environment.

MCP Settings Page

Navigate to Settings → MCP in your ZenML dashboard to access the configuration page. The page provides:

• Token configuration: Enter or generate the API token needed for authentication

• IDE-specific instructions: Tabbed configuration for VS Code, Claude Desktop, Cursor, Claude Code, OpenAI Codex, and other MCP clients

• Multiple installation methods: Deep links for automatic setup, CLI commands, and manual JSON configuration options

• Docker and uv options: Choose your preferred runtime for the MCP server

ZenML Pro vs OSS Setup Differences

Feature	ZenML Pro	ZenML OSS
Token generation	One-click PAT generation within the settings page	Paste a service account token (create via Settings → Service Accounts)
Project selection	Select which project to connect to	Single project (automatic)
Configuration output	Includes project ID in generated configs	Simplified configuration

ZenML Pro users can generate a Personal Access Token (PAT) directly from the MCP settings page with a single click. The token will be automatically included in the generated configuration snippets.

ZenML OSS users need to first create a service account token via Settings → Service Accounts, then paste it into the MCP settings page.

Manual Setup

For manual setup or the most up-to-date instructions, 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.

Prerequisites:

• Access to a ZenML server (Cloud or self-hosted)

• 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, Cursor, VS Code, etc.)

For detailed manual 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!