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mlflowAgent MCP Server

@iRahulPandey

Repository

Basic information

Category

Other

Transports

stdio

Author

iRahulPandey

About mlflowAgent MCP Server

mcp server for mlflow

#mlflow#mlflow-agent#mcp-server

mlflowAgent MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "mlflowAgent": {
      "command": "npx",
      "args": [
        "-y",
        "@smithery/cli",
        "install",
        "@iRahulPandey/mlflowMCPServer",
        "--client",
        "claude"
      ]
    }
  }
}

mlflowAgent MCP Server tools

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This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

Fetch the live tool list by running this server in a temporary sandbox using the button above.

Overview

What is mlflowAgent?

mlflowAgent is a natural language interface for MLflow built on the Model Context Protocol (MCP). It connects to your MLflow tracking server and lets you query experiments, models, and system information using plain English. It consists of an MCP server (mlflow_server.py) that exposes MLflow functionality via MCP and an MCP client (mlflow_client.py) that uses an LLM (OpenAI) to translate natural language queries into tool calls.

How to use mlflowAgent?

Install via Smithery (npx -y @smithery/cli install @iRahulPandey/mlflowMCPServer --client claude) or manually (clone repo, create venv, install dependencies). Set OPENAI_API_KEY and optionally MLFLOW_TRACKING_URI. Start the MCP server with python mlflow_server.py, then send queries like python mlflow_client.py "Show me all registered models in MLflow".

Key features of mlflowAgent

  • Natural language queries to your MLflow tracking server
  • List and explore experiments and runs
  • Get details about registered models in the registry
  • Retrieve MLflow system status and metadata

Use cases of mlflowAgent

  • Ask "What models do I have registered?" without remembering MLflow CLI commands
  • Quickly list all experiments from a conversation interface
  • Get model details (e.g. for "iris-classifier") by typing a simple question
  • Check whether your MLflow server is running and responsive

Frequently asked questions

1

What are the prerequisites for running mlflowAgent?

Python 3.8+, an MLflow tracking server (default `http://localhost:8080`), and an OpenAI API key for the LLM component.

2

How do I configure the MLflow tracking server URI?

Set the environment variable `MLFLOW_TRACKING_URI` (default: `http://localhost:8080`). You can also set `MODEL_NAME`, `MLFLOW_SERVER_SCRIPT`, and `LOG_LEVEL` via environment variables.

3

What MLflow operations does mlflowAgent support?

It supports listing models (`list_models`), listing experiments (`list_experiments`), getting model details (`get_model_details`), and getting system info (`get_system_info`). It currently covers a subset of MLflow functionality.

4

Does mlflowAgent require internet access?

Yes, the client uses OpenAI models, so internet access is needed for the natural language processing component.

5

Are there any known limitations?

The tool supports only a subset of MLflow features. Error handling may be limited for complex operations. Future improvements may add model predictions, visualization, and run management.

Comments

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