Other MCP Server · MODEL CONTEXT PROTOCOL
allen munsch mcp prefect
https://pypi.org/project/mcp-prefect/0.1.0/
WHAT IT CONNECTS
What allen munsch mcp prefect gives an AI Agent
- **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
- "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?"
- Add the function to the appropriate module in `src/mcp_prefect`
- Add the function to the `get_all_functions()` list in the module
- Add the new type to `APIType` in `enums.py`
- Create a new module in `src/prefect/`
- Update `main.py` to include the new API type
SECURITY
An MCP listing is not a security review
An MCP server may receive model context, credentials, local files, or permission to call external systems. Review its code, requested environment variables, network behavior, package provenance, and maintenance status before connecting it to an agent.
README
Repository documentation
I've personally moved my efforts to a more generic OpenAPI spec based MCP: https://github.com/allen-munsch/yas-mcp
Additionally, there is actually an official beta release by prefect over here: https://pypi.org/project/prefect-mcp/
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 uppip install mcp-prefectgit 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
- "Show me all my flows and their last run status"
- "Create a new flow run for the 'data-processing' deployment"
- "What's the current status of flow run 'abc-123'?"
Deployment Control
- "Pause the schedule for the 'daily-reporting' deployment"
- "Update the 'etl-pipeline' deployment with new parameters"
Infrastructure Management
- "List all work pools and their current status"
- "Create a new work queue for high-priority jobs"
Variable & Configuration
- "Create a variable called 'api_timeout' with value 300"
- "Show me all variables containing 'config' in their name"
Monitoring & Debugging
- "Get the logs for the last failed flow run"
- "Show me all running task runs right now"
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/ -vgit clone https://github.com/allen-munsch/mcp-prefect
cd mcp-prefect
pip install -e .
python -m mcp_prefectCOMMUNITY
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