mlflowAgent
About
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 (mlflowserver.py) that…
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- Author
- iRahulPandey
- GitHub stars
- 11
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Jump to
- 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
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
mlflowAgentCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
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".
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"mlflowagent": {
"mlflowAgent": {
"command": "npx",
"args": [
"-y",
"@smithery/cli",
"install",
"@iRahulPandey/mlflowMCPServer",
"--client",
"claude"
]
}
}
}
}McpServers
{
"mlflowAgent": {
"command": "npx",
"args": [
"-y",
"@smithery/cli",
"install",
"@iRahulPandey/mlflowMCPServer",
"--client",
"claude"
]
}
}MLflow MCP Server: Natural Language Interface for MLflow
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.
Overview
MLflow MCP Agent consists of two main components:
1. MLflow MCP Server (mlflow_server.py): Connects to your MLflow tracking server and exposes MLflow functionality through the Model Context Protocol (MCP).
2. MLflow MCP Client (mlflow_client.py): Provides a natural language interface to interact with the MLflow MCP Server using a conversational AI assistant.
Features
- Natural Language Queries: Ask questions about your MLflow tracking server in plain English
- Model Registry Exploration: Get information about your registered models
- Experiment Tracking: List and explore your experiments and runs
- System Information: Get status and metadata about your MLflow environment
Prerequisites
- Python 3.8+
- MLflow server running (default: http://localhost:8080)
- OpenAI API key for the LLM
Installation
Installing via Smithery
To install MLflow Natural Language Interface Server for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @iRahulPandey/mlflowMCPServer --client claude
Manual Installation
1. Clone this repository: git clone https://github.com/iRahulPandey/mlflowMCPServer.git
cd mlflowMCPServer
2. Create a virtual environment:
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
3. Install the required packages:
pip install mcp[cli] langchain-mcp-adapters langchain-openai langgraph mlflow
4. Set your OpenAI API key:
export OPENAI_API_KEY=your_key_here
5. (Optional) Configure the MLflow tracking server URI:
export MLFLOW_TRACKING_URI=http://localhost:8080
Usage
Starting the MCP Server
First, start the MLflow MCP server:
python mlflow_server.py
The server connects to your MLflow tracking server and exposes MLflow functionality via MCP.
Making Queries
Once the server is running, you can make natural language queries using the client:
python mlflow_client.py "What models do I have registered in MLflow?"
Example Queries:
- "Show me all registered models in MLflow"
- "List all my experiments"
- "Get details for the model named 'iris-classifier'"
- "What's the status of my MLflow server?"
Configuration
You can customize the behavior using environment variables:
- MLFLOW_TRACKING_URI: URI of your MLflow tracking server (default: http://localhost:8080)
- OPENAI_API_KEY: Your OpenAI API key
- MODEL_NAME: The OpenAI model to use (default: gpt-3.5-turbo-0125)
- MLFLOW_SERVER_SCRIPT: Path to the MLflow MCP server script (default: mlflow_server.py)
- LOG_LEVEL: Logging level (default: INFO)
MLflow MCP Server (mlflow_server.py)
The server connects to your MLflow tracking server and exposes the following tools via MCP:
- list_models: Lists all registered models in the MLflow model registry
- list_experiments: Lists all experiments in the MLflow tracking server
- get_model_details: Gets detailed information about a specific registered model
- get_system_info: Gets information about the MLflow tracking server and system
Limitations
- Currently only supports a subset of MLflow functionality
- The client requires internet access to use OpenAI models
- Error handling may be limited for complex MLflow operations
Future Improvements
- Add support for MLflow model predictions
- Improve the natural language understanding for more complex queries
- Add visualization capabilities for metrics and parameters
- Support for more MLflow operations like run management and artifact handling
Acknowledgments
- Model Context Protocol (MCP): For the protocol specification
- LangChain: For the agent framework
- MLflow: For the tracking and model registry functionality
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