M
MLflow
Query MLflow experiments, runs and models.
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About
Auto-generated
MLflow is a Model Context Protocol server in the Analytics & Monitoring category. It lets AI assistants that speak MCP call its tools and read its resources over STDIO.
Example tools
Illustrative tool names — the actual tool set is defined by the server at runtime.
list_resources
Enumerate resources exposed by this MCP
get_resource
Fetch a resource by ID
invoke
Invoke the primary action of this MCP
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Frequently Asked Questions
Common questions about MLflow.
- What is the primary function of the MLflow MCP?
- The MLflow MCP allows users to query MLflow experiments, runs, and models. It integrates MLflow's capabilities for tracking machine learning development into an MCP client.
- How do I configure the MLflow MCP in an MCP client?
- To configure the MLflow MCP, you will typically provide the API key. Specific configuration steps will vary depending on the MCP client you are using, such as Claude Desktop or Cursor.
- Does the MLflow MCP require an API key for authentication?
- Yes, the MLflow MCP uses an API key for authentication. You will need to provide a valid API key to access its functionalities.
- What are some typical use cases for the MLflow MCP?
- Typical use cases include reviewing the results of ML experiments, comparing different model runs, and managing registered models. It streamlines the monitoring and analysis of ML workflows.
- Is there a cost associated with using the MLflow MCP?
- The MLflow MCP itself is provided by the Community and is open-source. Any costs would depend on the underlying MLflow deployment and infrastructure you utilize.
- What is a prerequisite for using the MLflow MCP?
- A key prerequisite for using the MLflow MCP is having an existing MLflow deployment with experiments, runs, or models to query. The MCP acts as an interface to this existing MLflow instance.
Install MLflow
Claude Desktop
Add this to claude_desktop_config.json.
{
"mcpServers": {
"mlflow": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-mlflow"
]
}
}
}Cursor
Add this to ~/.cursor/mcp.json.
{
"mcpServers": {
"mlflow": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-mlflow"
]
}
}
}VS Code
Add this to your workspace settings.json.
{
"mcp.servers": {
"mlflow": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-mlflow"
]
}
}
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