MCP Server for ZenML

@zenml-io

Repository Homepage

Basic information

About MCP Server for ZenML

MCP server to connect an MCP client (Cursor, Claude Desktop etc) with your ZenML MLOps and LLMOps pipelines

#mcp#mlops#llmops #mcp-server

MCP Server for ZenML config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "mcp-zenml": {
      "command": "docker",
      "args": [
        "build",
        "-t",
        "mcp-zenml:apps",
        "."
      ]
    }
  }
}

MCP Server for ZenML tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is MCP Server for ZenML?

MCP Server for ZenML is an MCP server that provides standardized tools for Large Language Models to interact with the ZenML API. It allows AI applications to read core entities (users, stacks, pipelines, models) and trigger pipeline runs, making live ML platform data accessible through the Model Context Protocol.

How to use MCP Server for ZenML?

The easiest way is through the ZenML dashboard's MCP Settings page, which provides pre-configured snippets and one-click installation. For manual setup, you can run the server via Docker (especially for MCP Apps with Streamable HTTP transport) or using uv. Configuration requires a ZenML server URL, API key, and optionally an active project ID.

Key features of MCP Server for ZenML

List and get details on users, stacks, components, and flavors
Browse pipeline definitions, runs, steps, schedules, and artifacts
Read metadata about models, model versions, services, and deployments
Trigger new pipeline runs using snapshots or run templates
Diagnose server setup with environment and connectivity checks
Open interactive dashboards via experimental MCP Apps

Use cases of MCP Server for ZenML

Discover project context and find runnable snapshots for a pipeline
Trigger a new pipeline run and monitor its deployment status
Analyze recent pipeline runs and stack component usage
Retrieve step logs, source code, and artifact metadata for debugging
Diagnose ZenML server connectivity and authentication issues

Frequently asked questions

What is MCP?

MCP (Model Context Protocol) is an open standard that lets AI applications connect to data sources and tools in a uniform way, like a “USB-C port for AI.”

What is ZenML?

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

What transport does the server use for MCP Apps?

MCP Apps require Streamable HTTP transport; they cannot work via stdio.

Which AI clients currently support MCP Apps?

VS Code (Insiders), Goose, and ChatGPT (launching soon). Claude Desktop and Claude.ai do not yet support Apps as of late January 2026.

How do I migrate from Run Templates to Snapshots?

Replace `list_run_templates()` with `list_snapshots(runnable=True, named_only=True)`, `get_run_template(name)` with `get_snapshot(name, include_config_schema=True)`, and `trigger_pipeline(template_id=...)` with `trigger_pipeline(snapshot_name_or_id=...)`.

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