Mcp Prefect

What is Mcp Prefect about?

Mcp Prefect implements a Model Context Protocol (MCP) server for Prefect, allowing AI assistants to perform common Prefect operations using natural‑language commands.

How to use Mcp Prefect?

1. Configure the Prefect connection via environment variables:

   export PREFECT_API_URL="http://localhost:4200/api"
   export PREFECT_API_KEY="your_api_key"
   

2. Start the server (the repository includes a Docker Compose setup):

   docker compose up
   

3. Connect an AI assistant that understands the MCP specification to the running server. The assistant can then issue commands such as "Show me all my flows" or "Trigger the 'data-processing' deployment".

Key Features

• Flow Management – list, retrieve, and delete flows.
• Flow Run Management – create, monitor, and control flow runs.
• Deployment Management – manage deployments and their schedules, including triggering and pausing.
• Task Run Management – observe and control individual task runs.
• Work Queue Management – create and administer work queues.
• Block Management – access block types and block documents.
• Variable Management – create and update Prefect variables.
• Workspace Management – retrieve workspace information.

Use Cases

• An AI‑powered chatbot helps data engineers quickly inspect the status of their pipelines without opening the Prefect UI.
• Automated monitoring agents query failed flow runs and raise alerts in Slack or email.
• Natural‑language driven orchestration scripts trigger deployments on demand (e.g., "Run the nightly ETL now").
• Self‑service portals allow non‑technical users to pause or resume schedules using plain English.

FAQ

Q: Do I need Prefect Cloud? A: No. The server works with any Prefect deployment reachable via PREFECT_API_URL; a Cloud API key is only required for Cloud instances.

Q: Can I extend the server with new Prefect APIs? A: Yes. Add the function to the appropriate module under src/mcp_prefect and include it in the get_all_functions() list. For new API categories, update APIType in enums.py and modify main.py.

Q: Which transport protocol is supported? A: The example configuration shows using Server‑Sent Events (--transport sse). Additional transports can be added as needed.

Q: How are environment variables passed to the server? A: They are set in the container or host environment before launching the server, as shown in the configuration step above.

Q: What if some endpoints are missing? A: The README notes that several endpoints are yet to be implemented; contributions are welcome to fill those gaps.

Deprecated 27-Nov-2025

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/

Prefect MCP Server

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.

🚀 Quick Start

docker compose up

📦 Installation

pip Installation

pip install mcp-prefect

From Source

git clone https://github.com/allen-munsch/mcp-prefect
cd mcp-prefect
pip install -e .

Manual Run

PREFECT_API_URL=http://localhost:4200/api \
PREFECT_API_KEY=your_api_key_here \
MCP_PORT=8000 \
python -m mcp_prefect.main --transport http

🛠️ Features

╭────────────────────────────────────────────────────────────────────────────╮
│                                                                            │
│        _ __ ___  _____           __  __  _____________    ____    ____     │
│       _ __ ___ .'____/___ ______/ /_/  |/  / ____/ __ \  |___ \  / __ \    │
│      _ __ ___ / /_  / __ `/ ___/ __/ /|_/ / /   / /_/ /  ___/ / / / / /    │
│     _ __ ___ / __/ / /_/ (__  ) /_/ /  / / /___/ ____/  /  __/_/ /_/ /     │
│    _ __ ___ /_/    \____/____/\__/_/  /_/\____/_/      /_____(*)____/      │
│                                                                            │
│                                                                            │
│                                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

💬 Example Interactions

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"

🤖 Platform Integration

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "prefect": {
      "command": "mcp-prefect",
      "args": ["--transport", "stdio"]
    }
  }
}

Cursor MCP

{
  "mcpServers": {
    "prefect": {
      "command": "mcp-prefect",
      "args": ["--transport", "stdio"]
    }
  }
}

Gemini CLI

gemini config set mcp-servers.prefect "mcp-prefect --transport stdio"

Windsurf / Claude Code

{
  "mcpServers": {
    "prefect": {
      "command": "mcp-prefect",
      "args": ["--transport", "stdio"],
      "env": {
        "PREFECT_API_URL": "http://localhost:4200/api",
        "PREFECT_API_KEY": "your_api_key_here"
      }
    }
  }
}

Generic MCP Client

{
  "mcpServers": {
    "prefect": {
      "command": "mcp-prefect",
      "args": ["--transport", "stdio"],
      "env": {
        "PREFECT_API_URL": "http://localhost:4200/api",
        "PREFECT_API_KEY": "your_api_key_here"
      }
    }
  }
}

🧪 Development

Running Tests

pytest tests/ -v

Building from Source

git clone https://github.com/allen-munsch/mcp-prefect
cd mcp-prefect
pip install -e .
python -m mcp_prefect