Prefect
HTTP-SSE通过自然语言与Prefect工作流交互的服务器
通过自然语言与Prefect工作流交互的服务器
A Model Context Protocol (MCP) server implementation for Prefect, enabling AI assistants to interact with Prefect through natural language.
Note: The official Prefect MCP server is available here. This is a community implementation.
docker compose up
pip install mcp-prefect
git clone https://github.com/allen-munsch/mcp-prefect cd mcp-prefect pip install -e .
PREFECT_API_URL=http://localhost:4200/api \ PREFECT_API_KEY=your_api_key_here \ MCP_PORT=8000 \ python -m mcp_prefect.main --transport http
╭────────────────────────────────────────────────────────────────────────────╮
│ │
│ _ __ ___ _____ __ __ _____________ ____ ____ │
│ _ __ ___ .'____/___ ______/ /_/ |/ / ____/ __ \ |___ \ / __ \ │
│ _ __ ___ / /_ / __ `/ ___/ __/ /|_/ / / / /_/ / ___/ / / / / / │
│ _ __ ___ / __/ / /_/ (__ ) /_/ / / / /___/ ____/ / __/_/ /_/ / │
│ _ __ ___ /_/ \____/____/\__/_/ /_/\____/_/ /_____(*)____/ │
│ │
│ │
│ FastMCP 2.0 │
│ │
│ │
│ 🖥️ Server name: MCP Prefect 3.6.1 │
│ 📦 Transport: STDIO │
│ │
│ 🏎️ FastMCP version: 2.12.3 │
│ 🤝 MCP SDK version: 1.14.1 │
│ │
│ 📚 Docs: https://gofastmcp.com │
│ 🚀 Deploy: https://fastmcp.cloud │
│ │
╰────────────────────────────────────────────────────────────────────────────╯
[11/11/25 02:08:06] INFO Starting MCP server 'MCP Prefect 3.6.1' with transport 'stdio' server.py:1495
✅ Initialized successfully
Server: MCP Prefect 3.6.1 1.14.1
🔄 Listing tools...
🎯 FOUND 64 TOOLS:
================================================================================
📂 ARTIFACT (6 tools)
🔧 create_artifact
🔧 delete_artifact
🔧 get_artifact
🔧 get_artifacts
🔧 get_latest_artifacts
🔧 update_artifact
📂 AUTOMATION (7 tools)
🔧 create_automation
🔧 delete_automation
🔧 get_automation
🔧 get_automations
🔧 pause_automation
🔧 resume_automation
🔧 update_automation
📂 BLOCK (5 tools)
🔧 delete_block_document
🔧 get_block_document
🔧 get_block_documents
🔧 get_block_type
🔧 get_block_types
📂 DEPLOYMENT (8 tools)
🔧 delete_deployment
🔧 get_deployment
🔧 get_deployment_schedule
🔧 get_deployments
🔧 pause_deployment_schedule
🔧 resume_deployment_schedule
🔧 set_deployment_schedule
🔧 update_deployment
📂 FLOW (13 tools)
🔧 cancel_flow_run
🔧 create_flow_run_from_deployment
🔧 delete_flow
🔧 delete_flow_run
🔧 get_flow
🔧 get_flow_run
🔧 get_flow_run_logs
🔧 get_flow_runs
🔧 get_flow_runs_by_flow
🔧 get_flows
🔧 get_task_runs_by_flow_run
🔧 restart_flow_run
🔧 set_flow_run_state
📂 LOG (2 tools)
🔧 create_log
🔧 get_logs
📂 OTHER (1 tools)
🔧 get_health
📂 TASK (4 tools)
🔧 get_task_run
🔧 get_task_run_logs
🔧 get_task_runs
🔧 set_task_run_state
📂 VARIABLE (5 tools)
🔧 create_variable
🔧 delete_variable
🔧 get_variable
🔧 get_variables
🔧 update_variable
📂 WORK (13 tools)
🔧 create_work_queue
🔧 delete_work_queue
🔧 get_current_workspace
🔧 get_work_queue
🔧 get_work_queue_by_name
🔧 get_work_queue_runs
🔧 get_work_queues
🔧 get_workspace
🔧 get_workspace_by_handle
🔧 get_workspaces
🔧 pause_work_queue
🔧 resume_work_queue
🔧 update_work_queue
📊 TOTAL: 64 tools across 10 categories
AI assistants can help you with:
Flow Management
Deployment Control
Infrastructure Management
Variable & Configuration
Monitoring & Debugging
Add to claude_desktop_config.json:
{ "mcpServers": { "prefect": { "command": "mcp-prefect", "args": ["--transport", "stdio"] } } }
{ "mcpServers": { "prefect": { "command": "mcp-prefect", "args": ["--transport", "stdio"] } } }
gemini config set mcp-servers.prefect "mcp-prefect --transport stdio"
{ "mcpServers": { "prefect": { "command": "mcp-prefect", "args": ["--transport", "stdio"], "env": { "PREFECT_API_URL": "http://localhost:4200/api", "PREFECT_API_KEY": "your_api_key_here" } } } }
{ "mcpServers": { "prefect": { "command": "mcp-prefect", "args": ["--transport", "stdio"], "env": { "PREFECT_API_URL": "http://localhost:4200/api", "PREFECT_API_KEY": "your_api_key_here" } } } }
pytest tests/ -v
git clone https://github.com/allen-munsch/mcp-prefect cd mcp-prefect pip install -e . python -m mcp_prefect