MLflow MCP Server
by iRahulPandey
This project provides a natural language interface to MLflow via the Model Context Protocol (MCP). It allows you to query your MLflow tracking server using plain English, making it easier to manage and explore your machine learning experiments and models.
Last updated: N/A
What is MLflow MCP Server?
MLflow MCP Server is a system that connects to your MLflow tracking server and exposes MLflow functionality through the Model Context Protocol (MCP), providing a natural language interface for querying and managing your machine learning experiments and models.
How to use MLflow MCP Server?
First, start the MLflow MCP server using python mlflow_server.py. Then, make natural language queries using the client with python mlflow_client.py "Your Query". Ensure you have configured the necessary environment variables like OPENAI_API_KEY and MLFLOW_TRACKING_URI.
Key features of MLflow MCP Server
Natural Language Queries
Model Registry Exploration
Experiment Tracking
System Information
Use cases of MLflow MCP Server
Exploring registered models in MLflow using natural language
Listing and exploring MLflow experiments and runs
Getting details about specific models
Checking the status of the MLflow server
FAQ from MLflow MCP Server
What is the default MLflow tracking URI?
What is the default MLflow tracking URI?
The default MLflow tracking URI is http://localhost:8080.
What OpenAI model is used by default?
What OpenAI model is used by default?
The default OpenAI model is gpt-3.5-turbo-0125.
What are the limitations of this server?
What are the limitations of this server?
Currently only supports a subset of MLflow functionality, requires internet access for OpenAI models, and error handling may be limited for complex MLflow operations.
What is the Model Context Protocol (MCP)?
What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is a protocol specification that enables communication between different components in a machine learning system.
What are the future improvements planned for this server?
What are the future improvements planned for this server?
Future improvements include adding support for MLflow model predictions, improving natural language understanding, adding visualization capabilities, and supporting more MLflow operations like run management and artifact handling.