MCP Server for Prefect
Project description
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:
- Add the function to the appropriate module in
src/mcp_prefect - Add the function to the
get_all_functions()list in the module
To add a new API type:
- Add the new type to
APITypeinenums.py - Create a new module in
src/prefect/ - Update
main.pyto 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"
}
}
}
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