# Leveraging MCP ZenML server supports a chat interface that allows you to interact with the server using natural language through the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/). 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](https://3621652509-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F75OYotLPi8TviSrtZTJZ%2Fuploads%2Fgit-blob-72e3afdd3cdf7abd999808a688fe424530c05944%2Fmcp-zenml.png?alt=media) ## 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: [![ZenML MCP Server Features](https://3621652509-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F75OYotLPi8TviSrtZTJZ%2Fuploads%2Fgit-blob-25a985f15928055d4800b55937019a591490ae9c%2Fmcp-video.png?alt=media)](https://www.loom.com/share/4cac0c90bd424df287ed5700e7680b14?sid=200acd11-2f1b-4953-8577-6fe0c65cad3c) ## 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](https://3621652509-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F75OYotLPi8TviSrtZTJZ%2Fuploads%2Fgit-blob-bcf052b9be2d2855f557ec5e006f6ad470cfaf4f%2Fmcp-settings-page.gif?alt=media) 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 | {% hint style="info" %} **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. {% endhint %} ### Manual Setup For manual setup or the most up-to-date instructions, please refer to the [ZenML MCP Server GitHub repository](https://github.com/zenml-io/mcp-zenml). 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`](https://docs.astral.sh/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](https://github.com/zenml-io/mcp-zenml). ## 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](https://3621652509-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F75OYotLPi8TviSrtZTJZ%2Fuploads%2Fgit-blob-8cd259d4c778ebd9e2b177708c163363952e06cf%2Fmcp-pipeline-analysis.png?alt=media) 2. **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](https://3621652509-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F75OYotLPi8TviSrtZTJZ%2Fuploads%2Fgit-blob-7d93371ad703eb46720a4cbe21dca78a64aff1ef%2Fmcp-comparative-analysis.png?alt=media) 3. **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](https://3621652509-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F75OYotLPi8TviSrtZTJZ%2Fuploads%2Fgit-blob-0f823e370960818569e48ff1b08bc6e3c479a349%2Fmcp_stack_component_analysis.gif?alt=media) ## Get Involved We invite you to try the [ZenML MCP Server](https://github.com/zenml-io/mcp-zenml) and share your experiences with us through our [Slack community](https://zenml.io/slack). 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](https://github.com/zenml-io/mcp-zenml) are always welcome!
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