What are the runtime dependencies?
Node.js and npm to build and run the server, plus an MLflow server (≥2.21.1) hosting the Prompt Registry.
@B-Step62
Basic information
MCP server for accessing prompts stored in MLflow Prompt Registry
No standard config provided
This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.
RepositoryList available prompts
Retrieve and compile a specific prompt
It is a Model Context Protocol (MCP) server that connects to MLflow Prompt Registry, allowing AI assistants (like Claude Desktop) to discover and load prompt templates stored in MLflow. It implements the MCP Prompts specification for retrieving and using reusable prompt templates.
Install MLflow (≥2.21.1), start an MLflow server, create a prompt template in MLflow, then build this MCP server (npm install && npm run build). Finally, add it to Claude Desktop by editing claude_desktop_config.json with the node command pointing to dist/index.js and setting the MLFLOW_TRACKING_URI environment variable to your MLflow server address.
list-prompts and get-promptNode.js and npm to build and run the server, plus an MLflow server (≥2.21.1) hosting the Prompt Registry.
Prompt templates are stored and managed in the MLflow server (Prompt Registry). The MCP server reads them via the `MLFLOW_TRACKING_URI` environment variable.
The server connects over stdio (standard input/output). No HTTP or WebSocket transport is mentioned.
The README does not mention any authentication. It assumes a local or accessible MLflow server without auth.
The README does not list limits. Output size depends on prompt template size and number of prompts returned by MLflow.
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