The MLflow MCP Server exposes MLflow experiment tracking and model registry operations as tools for AI assistants via the Model Context Protocol (MCP). It connects to any MLflow tracking server via the MLFLOW_TRACKING_URI environment variable and is compatible with clients like Claude Desktop, Cursor, and OpenCode.
Experiment Management:
Retrieve experiment details by ID (
get_experiment) or name (get_experiment_by_name)Search/list experiments with optional name filtering and pagination (
search_experiments)
Run Management:
Fetch full details of a run (metrics, parameters, tags) by ID (
get_run)List all runs for a given experiment with pagination (
get_experiment_runs)
Model Registry:
Search and list registered models with optional filtering and pagination (
get_registered_models)Browse model versions with filtering and pagination (
get_model_versions)Create new registered models with optional description and tags (
create_registered_model)Create new model versions from a run's artifacts (
create_model_version)Rename an existing registered model (
rename_registered_model)Assign aliases (e.g.,
champion,challenger) to model versions (set_registered_model_alias)Delete a registered model and all its versions (
delete_registered_model)Delete a specific model version (
delete_model_version)
Provides tools for interacting with MLflow experiments, runs, and registered models, enabling browsing of experiments, retrieving run details with metrics and parameters, and querying the model registry with filtering and pagination support.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MLflow MCP Servershow me all experiments containing 'recommendation' in the name"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MLflow MCP Server
A Model Context Protocol (MCP) server that exposes MLflow experiment tracking and model registry operations as tools for AI assistants.
Table of Contents
Quickstart
The fastest way to get started is to add the server to your MCP client config. No local clone required.
Claude Desktop
Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):
{
"mcpServers": {
"mlflow": {
"command": "uvx",
"args": ["mlflow-mcp-server"],
"env": {
"MLFLOW_TRACKING_URI": "http://localhost:5000"
}
}
}
}Cursor
Add to ~/.cursor/mcp.json:
{
"mcpServers": {
"mlflow": {
"command": "uvx",
"args": ["mlflow-mcp-server"],
"env": {
"MLFLOW_TRACKING_URI": "http://localhost:5000"
}
}
}
}OpenCode
Add to your opencode.json:
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"mlflow": {
"type": "local",
"command": ["uvx", "mlflow-mcp-server"],
"environment": {
"MLFLOW_TRACKING_URI": "http://localhost:5000"
}
}
}
}Replace http://localhost:5000 with the URL of your MLflow tracking server.
Tools
Experiment Management
Tool | Description |
| Get experiment details by ID |
| Get experiment details by name |
| List and filter experiments with optional name matching and pagination |
Run Management
Tool | Description |
| Get full run details including metrics, parameters, tags, and run type (parent/child/standalone) |
| List runs for an experiment with pagination |
Model Registry
Tool | Description |
| Search and list registered models |
| Browse model versions with filtering |
| Create a new registered model with optional description and tags |
| Create a new model version from a run's artifacts |
| Rename an existing registered model |
| Assign an alias (e.g. |
| Delete a registered model and all its versions |
| Delete a specific model version |
Example Prompts
Once configured, you can ask your AI assistant things like:
Exploring experiments and runs:
"List all experiments related to recommendation models"
"Show me the runs for experiment 12 and compare their metrics"
"Get the parameters and metrics for run abc123"
"Which runs in the fraud-detection experiment have the highest accuracy?"
Managing the model registry:
"Show me all registered models"
"Register a new model called churn-classifier with description 'Binary classifier for customer churn'"
"Create a new version of churn-classifier from run abc123"
"Set the champion alias on version 3 of churn-classifier"
"Rename the model old-name to new-name"
"Delete version 1 of churn-classifier"
Analysis and comparison:
"Compare the last 5 runs of the search-ranking experiment by NDCG and latency"
"What hyperparameters were used in the best-performing run of experiment 7?"
"List all model versions for recommendation-model and their aliases"
Configuration
Environment Variable | Default | Description |
|
| URL of the MLflow tracking server |
Installation (Development)
Prerequisites
Python 3.11+
An MLflow tracking server
Setup
git clone https://github.com/yesid-lopez/mlflow-mcp-server.git
cd mlflow-mcp-server
uv syncRunning Locally
export MLFLOW_TRACKING_URI="http://localhost:5000"
uv run -m mlflow_mcp_serverThe server communicates over stdio, which is the standard MCP transport for local tool servers.
Project Structure
mlflow_mcp_server/
├── __main__.py # Entry point
├── server.py # MCP server setup and tool registration
├── tools/
│ ├── experiment_tools.py # Experiment search and retrieval
│ ├── run_tools.py # Run details and listing
│ └── registered_models.py # Model registry CRUD operations
└── utils/
└── mlflow_client.py # MLflow client singletonAdding New Tools
Create a function in the appropriate file under
tools/.Register it in
server.py:
from mlflow_mcp_server.tools.your_module import your_function
mcp.add_tool(your_function)Linting
uv run ruff check .
uv run ruff format --check .License
MIT
Resources
Looking for Admin?
Admins can modify the Dockerfile, update the server description, and track usage metrics. If you are the server author, to access the admin panel.