Summary
Live answers about your ML pipelines, from ZenML itself.
It is mostly read: what ran, what failed, which stack it ran on, what is serving right now and what those deployments are logging. The one action it offers is triggering a pipeline from an existing snapshot, so a rerun does not need the original code in front of you.
What it is
ZenML's own MCP server over the ZenML API: live read access to pipelines, runs, steps, stacks, artifacts and deployments, plus triggering a pipeline from a snapshot.
What you get
- Deployments listed, with status, URL and logs for any one of them
- Snapshots — the frozen pipeline configurations — listed and read
- A pipeline triggered from an existing snapshot
- The active project reported, so calls land where you think they do
- Read access to the core entities: users, stacks, stack components, flavors, service connectors
- Pipeline runs, steps, step code, logs and schedules
- Artifact metadata — the metadata, not the data — plus tags, builds and services
Requirements
A running ZenML server to point it at, and credentials for that server's API.
Setup effort
One command plus a key — docker pull zenmldocker/mcp-zenml:latest, then supply credentials