About MLflow Prompt Registry MCP Server
MLflow Prompt Registry MCP Server gives MCP clients access to prompt templates stored in MLflow. It supports discovering prompts and retrieving a selected template with its variables compiled, for workflows such as repeated tasks in Claude Desktop.
Key features
- 01List available prompts
- 02Filter and paginate prompt listings
- 03Retrieve a named prompt
- 04Compile prompts with variables
Use cases
- 01Finding a prompt in MLflow
- 02Loading a template for a repeated task
- 03Filling prompt variables before use
MCP support
The server implements the MCP Prompts specification, exposing MLflow prompt templates through list and retrieval operations. Clients can list prompts and fetch a compiled prompt with supplied arguments.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEMLflow Prompt Registry MCP Server
Model Context Protocol (MCP) Server for MLflow Prompt Registry, enabling access to prompt templates managed in MLflow.
This server implements the MCP Prompts specification for discovering and using prompt templates from MLflow Prompt Registry. The primary use case is to load prompt templates from MLflow in Claude Desktop, allowing users to instruct Claude conveniently for repetitive tasks or common workflows.

Tools
list-prompts- List available prompts
- Inputs:
cursor(optional string): Cursor for paginationfilter(optional string): Filter for prompts
- Returns: List of prompt objects
get-prompt- Retrieve and compile a specific prompt
- Inputs:
name(string): Name of the prompt to retrievearguments(optional object): JSON object with prompt variables
- Returns: Compiled prompt object
Setup
1: Install MLflow and Start Prompt Registry
Install and start an MLflow server if you haven't already to host the Prompt Registry:
pip install mlflow>=2.21.1
mlflow server --port 5000
2: Create a prompt template in MLflow
If you haven't already, create a prompt template in MLflow following this guide.
3: Build MCP Server
npm install
npm run build
4: Add the server to Claude Desktop
Configure Claude for Desktop by editing claude_desktop_config.json:
{
"mcpServers": {
"mlflow": {
"command": "node",
"args": ["<absolute-path-to-this-repository>/dist/index.js"],
"env": {
"MLFLOW_TRACKING_URI": "http://localhost:5000"
}
}
}
}
Make sure to replace the MLFLOW_TRACKING_URI with your actual MLflow server address.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.