Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@metaflow-mcp-server list recent runs of the RecommendationFlow"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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.
Tool | Description |
| What backend am I connected to? (also returns your default namespace) |
| What flows exist in a namespace? |
| Find recent runs of any flow |
| Step-by-step breakdown of a run |
| Pull stdout/stderr from a task |
| What did this step produce? |
| Grab an artifact's value |
| What broke and why? |
| Which runs produced a named artifact? |
Related MCP server: MLflow MCP Server
pip install metaflow-mcp-server
claude mcp add --scope user metaflow -- metaflow-mcp-serverThat's it. Restart Claude Code and start asking questions about your flows.
To upgrade:
pip install --upgrade metaflow-mcp-serverThen 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-serverFor other MCP clients, the server speaks stdio: metaflow-mcp-server
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.
Apache-2.0
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Cross-agent artifact workspace with provenance across Claude Code, Codex, Cursor, LangGraph.
Gateway between LLM agents and world data through eight tools and a bundled endpoint catalog.
Persistent memory and knowledge management for AI agents with semantic search and 50+ tools.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/npow/metaflow-mcp-server'
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