mcp-zenml is the Python MCP server maintained by the ZenML team for talking to a ZenML server, the open source platform for building and managing machine learning and AI pipelines. It is primarily a read interface onto live platform state, with one write path. Core entity tools cover users and permissions, stacks and the individual stack components that make them up, the flavours available for those components, and service connectors that hold cloud authentication. Pipeline execution tools reach pipeline definitions, run history and status, individual run steps together with their logs and source code, schedules for automated runs, and artifact metadata, though not artifact contents. A newer group covers deployment and serving: snapshots, described as frozen pipeline configurations that say what to run or serve, deployments with runtime status, URL and bounded log retrieval capped at a thousand lines, and model serving services. Organisation tools handle projects, cross-cutting tags and build artifacts with image and code embedding information, while a model group covers registry entries and model versions, and a secrets tool returns names only and never values. Analysis helpers summarise stack component usage and recent run activity, a diagnostic tool checks environment variables, SDK presence, connectivity and authentication and is designed to work even when the setup is broken, and two experimental tools open interactive MCP apps showing a runs dashboard and a thirty day activity chart. The single write capability is triggering a pipeline run, which now prefers a snapshot; the older run template tools are marked deprecated with a documented migration to snapshots. The README also explains the Model Context Protocol itself in terms of hosts, clients, servers and data sources. It is aimed at teams already operating ZenML who want conversational access to pipeline and deployment state.
https://github.com/zenml-io/mcp-zenml
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