mcp server for mlflow
If you already use MLflow : Natural Language Interface For MLflow, the mlflow : natural language interface for mlflow mcp server is the piece that lets your assistant work with it directly. mcp server 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.
mlflow_server.py): Connects to your MLflow tracking server and exposes MLflow functionality through the Model Context Protocol (MCP).@smithery/cli on npm is all you need. Most clients run it directly, so configuration is a few lines and a restart.
Configuration is passed through the environment: OPENAI_API_KEY, MLFLOW_TRACKING_URI. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
http://localhost:8080) - OpenAI API key for the LLMPlenty of developer tooling servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. It is maintained by iRahulPandey; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
{
"mcpServers": {
"mlflowmcpserver": {
"command": "npx",
"args": ["-y", "@smithery/cli"],
"env": {
"OPENAI_API_KEY": "your-value",
"MLFLOW_TRACKING_URI": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
http://localhost:8080) - OpenAI API key for the LLM| Variable | Description | Required |
|---|---|---|
| OPENAI_API_KEY | Credential the server authenticates with. | Yes |
| MLFLOW_TRACKING_URI | Configuration value read at startup. | Optional |
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