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

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

Set the following environment variables:

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

Run the MCP server, and prefect:

docker compose up

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?"

Several of the endpoints have yet to be implemented

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"
    }
  }
}