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

Project description

Metaflow MCP Server

CI PyPI Python License MCP

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.

demo

Tools

Tool Description
get_config What backend am I connected to?
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?

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.

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

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