mcp server for mlflow
Most developer tooling work still happens through a UI a human drives. MlflowAgent MCP server moves it into the conversation instead. 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).Installation goes through your MCP client rather than a global install: point it at @smithery/cli on npm and it is fetched when the client starts. The copy-paste blocks for Claude Desktop, Claude Code and Cursor are further down this page.
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 LLMAmong the developer tooling options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. It is maintained by iRahulPandey; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against MlflowAgent's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
{
"mcpServers": {
"mlflowagent": {
"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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