metaflow-mcp-server 0.2.1

MCP server for Metaflow -- expose flow runs, logs, and artifacts as tools for AI coding agents

Metaflow MCP Server

Give your coding agent superpowers over your Metaflow workflows. Instead of writing throwaway scripts to check run status or dig through logs, just ask -- your agent will figure out the rest.

Works with any Metaflow backend: local, S3, Azure, GCS, or Netflix internal.

Tools

Tool	Description
get_config	What backend am I connected to? (also returns your default namespace)
list_flows	What flows exist in a namespace?
search_runs	Find recent runs of any flow
get_run	Step-by-step breakdown of a run
get_task_logs	Pull stdout/stderr from a task
list_artifacts	What did this step produce?
get_artifact	Grab an artifact's value
get_latest_failure	What broke and why?
search_artifacts	Which runs produced a named artifact?

Quickstart

pip install metaflow-mcp-server
claude mcp add --scope user metaflow -- metaflow-mcp-server

That's it. Restart Claude Code and start asking questions about your flows.

To upgrade:

pip install --upgrade metaflow-mcp-server

Then restart Claude Code (or reconnect via /mcp) to pick up the new version.

If Metaflow lives in a specific venv, point to it:

claude mcp add --scope user metaflow -- /path/to/venv/bin/metaflow-mcp-server

For other MCP clients, the server speaks stdio: metaflow-mcp-server

How it works

Wraps the Metaflow client API. Whatever backend your Metaflow is pointed at, the server uses too -- no separate config needed. Sets namespace(None) at startup so production runs (Argo, Step Functions, Maestro) are visible alongside your dev runs.

Starts once per session, communicates over stdin/stdout. No daemon, no port.

License

Apache-2.0