What is Mcp Prefect

mcp-prefect is an implementation of a Model Context Protocol (MCP) server for Prefect, enabling AI assistants to interact with Prefect through natural language.

Use cases

Use cases include querying flow statuses, managing deployments, monitoring task runs, and interacting with Prefect using natural language commands.

How to use

To use mcp-prefect, set the necessary environment variables for the Prefect API URL and key, then run the MCP server using Docker with the command ‘docker compose up’.

Key features

Key features include flow management, flow run management, deployment management, task run management, work queue management, block management, variable management, and workspace management.

Where to use

mcp-prefect can be used in various fields that require workflow orchestration, such as data engineering, machine learning, and automation tasks.

Prefect MCP Server

A Model Context Protocol (MCP) server implementation for Prefect, allowing AI assistants to interact with Prefect through natural language.

Features

This MCP server provides access to the following Prefect APIs:

• Flow Management: List, get, and delete flows
• Flow Run Management: Create, monitor, and control flow runs
• Deployment Management: Manage deployments and their schedules
• Task Run Management: Monitor and control task runs
• Work Queue Management: Create and manage work queues
• Block Management: Access block types and documents
• Variable Management: Create and manage variables
• Workspace Management: Get information about workspaces

Configuration

Set the following environment variables:

export PREFECT_API_URL="http://localhost:4200/api"  # URL of your Prefect API
export PREFECT_API_KEY="your_api_key"               # Your Prefect API key (if using Prefect Cloud)

Usage

Run the MCP server, and prefect:

docker compose up

Example Input

Once connected, an AI assistant can help users interact with Prefect using natural language. Examples:

• “Show me all my flows”
• “List all failed flow runs from yesterday”
• “Trigger the ‘data-processing’ deployment”
• “Pause the schedule for the ‘daily-reporting’ deployment”
• “What’s the status of my last ETL flow run?”

Development

Several of the endpoints have yet to be implemented

Adding New Functions

To add a new function to an existing API:

1. Add the function to the appropriate module in src/mcp_prefect
2. Add the function to the get_all_functions() list in the module

To add a new API type:

1. Add the new type to APIType in enums.py
2. Create a new module in src/prefect/
3. Update main.py to include the new API type

Example usage:

{
  "mcpServers": {
    "mcp-prefect": {
      "command": "mcp-prefect",
      "args": [
        "--transport", "sse"
      ],
      "env": {
        "PYTHONPATH": "/path/to/your/project/directory"
      },
      "cwd": "/path/to/your/project/directory"
    }
  }
}