Modern workflow orchestration platform.
## Prefect MCP Server: Modern Workflow Orchestration The **Prefect MCP Server** integrates Prefect's next-generation workflow orchestration platform into Google Antigravity, enabling developers to build, schedule, and monitor data pipelines with AI-assisted workflow management. ### Why Prefect MCP? - **Pythonic Workflows**: Define workflows as native Python code with decorators - **Dynamic Pipelines**: Create dynamic workflows that adapt based on runtime conditions - **Hybrid Execution**: Run workflows locally, in the cloud, or in Kubernetes clusters - **Observability**: Built-in logging, retries, and state management with a beautiful UI - **Infrastructure as Code**: Define deployment configurations alongside workflow code ### Key Features #### 1. Flow Definition and Execution ```python from prefect import flow, task @task(retries=3, retry_delay_seconds=60) def extract_data(source: str): return fetch_from_source(source) @task def transform_data(raw_data: dict): return apply_transformations(raw_data) @task def load_data(data: dict, destination: str): write_to_destination(data, destination) @flow(name="ETL Pipeline") def etl_pipeline(source: str, destination: str): raw = extract_data(source) transformed = transform_data(raw) load_data(transformed, destination) return {"status": "success", "rows": len(transformed)} # Execute via MCP result = await prefect.runFlow("etl_pipeline", { "source": "s3://bucket/input/", "destination": "bigquery://project.dataset.table" }) ``` #### 2. Deployment and Monitoring ```python # Create deployment deployment = await prefect.createDeployment({ "name": "daily-etl", "flow_name": "ETL Pipeline", "schedule": {"cron": "0 6 * * *"}, "work_pool": "kubernetes-pool", "parameters": { "source": "s3://bucket/daily/", "destination": "warehouse.daily_data" } }) # Monitor flow runs runs = await prefect.getFlowRuns({ "flow_name": "ETL Pipeline", "state": ["RUNNING", "FAILED"], "limit": 10 }) ``` ### Configuration ```json { "mcpServers": { "prefect": { "command": "npx", "args": ["-y", "@anthropic/mcp-prefect"], "env": { "PREFECT_API_URL": "https://api.prefect.cloud/api", "PREFECT_API_KEY": "your-api-key", "PREFECT_WORKSPACE": "your-workspace" } } } } ``` ### Use Cases **Data Engineering**: Build production-grade ETL pipelines with automatic retries and failure handling. **ML Operations**: Orchestrate machine learning workflows from training to deployment with full observability. **Event-Driven Automation**: Create reactive workflows that respond to webhooks, file changes, or database events. The Prefect MCP Server brings modern Python-native workflow orchestration to your development environment.
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"prefect": {}
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