Video by MLflow via YouTube
Learn how to write, register, and manage custom and third-party MCP servers with the MLflow MCP registry.
The registry is a central place to register, version, discover, and alias MCP servers so tools can be tracked in an MLflow tracking server. It works similarly to a prompt registry.
This tutorial covers:
🔹 Three registry entities: MLflow Server (a logical server with a canonical namespace slug, for example io.github…), MCP Server 🔹 Version (snapshot and version a set of tools as they move from staging to development to production), and Access Point (how the MCP client connects, via standard IO locally over IPC or stream HTTP with JSON-RPC over HTTP)
🔹 Lifecycle stages: Draft, Active, Deprecated, and Deleted. An active server cannot be deleted until it has been deprecated first
🔹 Building a custom MCP server: a Python FastMCP example called "order analytics," an in-memory fake database, with tools created by decorating functions with @mcp.tool
🔹 Registering servers: JSON config for the custom server and a third-party Wikipedia server (deep-wiki), then querying them with the MCP client API and with natural language in Claude
🔹 Versioning in the UI: bump a version, apply a production alias, and keep multiple active versions (staging and production) side by side
🔹 Cleanup: a helper that kills active subprocesses and unregisters entities so the workflow stays idempotent
Notebook: https://github.com/dmatrix/mlflow-genai-tutorials/blob/main/mlflow_mcp_registry.ipynb
Speaker: Jules Damji, Developer Advocate, Databricks
00:00 – Introduction to the MLflow MCP Registry
01:06 – MLflow MCP Registry Architecture Overview
02:08 – Understanding the Three Registry Entities
04:58 – Environment Setup & Prerequisite Packages
06:02 – Building a Custom MCP Server in Python
08:33 – Registering a Custom Server in the MLflow UI
10:19 – Querying the Custom Server Using the Client API
12:58 – Registering and Accessing an External Server (Wikipedia)
14:24 – Querying the External Server via Natural Language (Claude)
16:44 – Managing Versions, Lifecycle Stages, and Workspace Cleanup
#MLflow #MCP #Databricks #GenAI #Python #Claude #ModelRegistry