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mlflowAgent MCP Server

@iRahulPandey

代码仓库

基本信息

分类

其他

传输方式

stdio

作者

iRahulPandey

关于 mlflowAgent MCP Server

mcp server for mlflow

#mlflow#mlflow-agent#mcp-server

mlflowAgent MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

mlflowAgent MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。

概览

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

常见问题

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.

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