MCP Server For ZenML

What is MCP Server For ZenML about?

MCP Server For ZenML connects an MCP client (e.g., Claude Desktop, Cursor) to a ZenML server, allowing the client to query and manipulate ZenML resources such as pipelines, runs, artifacts, and services via the standardized MCP interface.

How to use MCP Server For ZenML?

1. Prerequisites: Access to a ZenML server (cloud or self‑hosted) and uv installed locally.
2. Clone the repository:

   git clone https://github.com/zenml-io/mcp-zenml.git
   

3. Create an MCP config file (mcp.json or the JSON snippet required by your client) that points to the server script and supplies the ZenML store URL and API key.
4. Run the server (example using uv):

   uv run server/zenml_server.py
   

   The command can also be launched automatically by the client when the DXT package (mcp-zenml.dxt) is dragged into Claude Desktop.
5. Interact via the client: Once the server is running, use the MCP client to invoke ZenML tools—e.g., list pipelines, fetch step logs, or trigger a new pipeline run.

Key features of MCP Server For ZenML

• Access to core ZenML entities: users, stacks, pipelines, runs, steps, services, components, flavors, schedules, artifacts, and service connectors.
• Retrieval of step code and logs (including cloud‑based logs).
• Ability to trigger new pipeline runs when a run template exists.
• Automated smoke testing via GitHub Actions with issue creation on failures.
• Support for Anthropic Desktop Extensions (DXT) for one‑click installation.

Use cases of MCP Server For ZenML

• Interactive debugging: Query live pipeline run details and logs directly from an LLM‑powered IDE.
• Automated orchestration: Trigger pipelines from chat assistants based on natural‑language commands.
• Knowledge retrieval: Pull metadata about datasets, models, and artifacts to augment LLM responses.
• Team collaboration: Provide non‑technical stakeholders with AI‑driven summaries of ML workflow status.

FAQ from the MCP Server For ZenML

Q: Do I need a ZenML Cloud account? A: No, any reachable ZenML server works; a cloud trial is provided for quick start.

Q: Which Python package manager should I use? A: uv is recommended for fast, reproducible environment setup, but a standard pip‑based install also works.

Q: Can I run the server on Windows? A: Yes, as long as uv (or Python) and the ZenML server are accessible.

Q: How does authentication work? A: Provide a ZenML service‑account API key via the ZENML_STORE_API_KEY environment variable in the MCP config.

Q: What happens if the smoke test fails? A: GitHub Actions automatically opens an issue with debugging details.

MCP Server for ZenML

This project implements a Model Context Protocol (MCP) server for interacting with the ZenML API.

What is MCP?

The Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to Large Language Models (LLMs). It acts like 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 Hosts: Programs like Claude Desktop or IDEs that want to access data through MCP
• MCP Clients: Protocol clients that maintain 1:1 connections with servers
• MCP Servers: Lightweight programs that expose specific capabilities through the standardized protocol
• Local Data Sources: Your computer's files, databases, and services that MCP servers can securely access
• Remote Services: External systems available over the internet that MCP servers can connect to

What is ZenML?

ZenML is an open-source platform for building and managing ML and AI pipelines. It provides a unified interface for managing data, models, and experiments.

For more information, see the ZenML website and our documentation.

Features

The server provides MCP tools to access core read functionality from the ZenML server, providing a way 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 (if a run template is present).

Note: This is a beta/experimental release. We're still exploring how people will use this integration, so we welcome your feedback and suggestions! Please join our Slack community to share your experience and help us improve.

Testing & Quality Assurance

This project includes automated testing to ensure the MCP server remains functional:

• 🔄 Automated Smoke Tests: A comprehensive smoke test runs every 3 days via GitHub Actions
• 🚨 Issue Creation: Failed tests automatically create GitHub issues with detailed debugging information
• ⚡ Fast CI: Uses UV with caching for quick dependency installation and testing
• 🧪 Manual Testing: You can run the smoke test locally using uv run scripts/test_mcp_server.py server/zenml_server.py

The automated tests verify:

• MCP protocol connection and handshake
• Server initialization and tool discovery
• Basic tool functionality (when ZenML server is accessible)
• Resource and prompt enumeration

How to use

Prerequisites

You will need to have access to a deployed ZenML server. If you don't have one, you can sign up for a free trial at ZenML Pro and we'll manage the deployment for you.

You will also (probably) need to have uv installed locally. For more information, see the uv documentation. We recommend installation via their installer script or via brew if using a Mac. (Technically you don't need it, but it makes installation and setup easy.)

You will also need to clone this repository somewhere locally:

git clone https://github.com/zenml-io/mcp-zenml.git

Your MCP config file

The MCP config file is a JSON file that tells the MCP client how to connect to your MCP server. Different MCP clients will use or specify this differently. Two commonly-used MCP clients are Claude Desktop and Cursor, for which we provide installation instructions below.

You will need to specify your ZenML MCP server in the following format:

{
    "mcpServers": {
        "zenml": {
            "command": "/usr/local/bin/uv",
            "args": ["run", "path/to/server/zenml_server.py"],
            "env": {
                "LOGLEVEL": "INFO",
                "NO_COLOR": "1",
                "PYTHONUNBUFFERED": "1",
                "PYTHONIOENCODING": "UTF-8",
                "ZENML_STORE_URL": "https://your-zenml-server-goes-here.com",
                "ZENML_STORE_API_KEY": "your-api-key-here"
            }
        }
    }
}

There are four dummy values that you will need to replace:

• the path to your locally installed uv (the path listed above is where it would be on a Mac if you installed it via brew)
• the path to the zenml_server.py file (this is the file that will be run when you connect to the MCP server). This file is located inside this repository at the root. You will need to specify the exact full path to this file.
• the ZenML server URL (this is the URL of your ZenML server. You can find this in the ZenML Cloud UI). It will look something like https://d534d987a-zenml.cloudinfra.zenml.io.
• the ZenML server API key (this is the API key for your ZenML server. You can find this in the ZenML Cloud UI or read these docs on how to create one. For the purposes of the ZenML MCP server we recommend using a service account.)

You are free to change the way you run the MCP server Python file, but using uv will probably be the easiest option since it handles the environment and dependency installation for you.

Installation for use with Claude Desktop

You will need to have the latest version of Claude Desktop installed.

You can simply open the Settings menu and drag the mcp-zenml.dxt file from the root of this repository on top of the menu and it will guide you through the installation and setup process. You'll need to add your ZenML server URL and API key.

Optional: Improving ZenML Tool Output Display

For a better experience with ZenML tool results, you can configure Claude to display the JSON responses in a more readable format. In Claude Desktop, go to Settings → Profile, and in the "What personal preferences should Claude consider in responses?" section, add something like the following (or use these exact words!):

When using zenml tools which return JSON strings and you're asked a question, you might want to consider using markdown tables to summarize the results or make them easier to view!

This will encourage Claude to format ZenML tool outputs as markdown tables, making the information much easier to read and understand.

Installation for use with Cursor

You will need to have Cursor installed.

Cursor works slightly differently to Claude Desktop in that you specify the config file on a per-repository basis. This means that if you want to use the ZenML MCP server in multiple repos, you will need to specify the config file in each of them.

To set it up for a single repository, you will need to:

• create a .cursor folder in the root of your repository
• inside it, create a mcp.json file with the content above
• go into your Cursor settings and click on the ZenML server to 'enable' it.

In our experience, sometimes it shows a red error indicator even though it is working. You can try it out by chatting in the Cursor chat window. It will let you know if is able to access the ZenML tools or not.

Desktop Extensions (DXT) Support

This project supports Anthropic's Desktop Extensions (DXT) standard, which makes installing MCP servers as simple as clicking a button. DXT is a new packaging format that bundles entire MCP servers into a single .dxt file, including all dependencies and providing user-friendly configuration.

The mcp-zenml.dxt file in the repository root contains everything needed to run the ZenML MCP server, eliminating the need for complex manual installation steps. This makes powerful ZenML integrations accessible to users without requiring technical setup expertise.

When you drag and drop the .dxt file into Claude Desktop's settings, it automatically handles:

• Runtime dependency installation
• Secure configuration management
• Cross-platform compatibility
• User-friendly setup process

For more information about Desktop Extensions and the DXT standard, visit the official documentation.