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The Prefect MCP server enables AI assistants to interact with your Prefect workflows and infrastructure through the Model Context Protocol (MCP). This integration allows AI tools like Claude Code, Cursor, and Codex CLI to help you monitor deployments, debug flow runs, query infrastructure, and more.
The Prefect MCP server is currently in alpha. APIs, features, and behaviors may change without notice. We encourage you to try it out and provide feedback through GitHub issues.

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What is the Prefect MCP server?

The Prefect MCP server is an MCP server that provides AI assistants with tools to:
  • Monitor & inspect: View system health, query deployments, flow runs, task runs, work pools, and execution logs
  • Debug intelligently: Get contextual guidance for troubleshooting failed flows and deployment issues
  • Access documentation: Query up-to-date Prefect documentation through an integrated docs proxy
The MCP tools are primarily designed for reading data and monitoring your Prefect instance. For creating or updating resources, the integrated docs proxy provides AI assistants with current information on how to use the prefect CLI.

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Installation

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Local installation

Install and run the MCP server locally using uvx: 
uvx --from prefect-mcp prefect-mcp-server
When running locally with stdio transport, the server automatically inherits credentials from your active Prefect profile (~/.prefect/profiles.toml).

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Cloud deployment

Deploy the MCP server to FastMCP Cloud for remote access:
  1. Fork the prefect-mcp-server repository on GitHub
  2. Sign in to fastmcp.cloud
  3. Create a new server pointing to your fork:
    • Server path: src/prefect_mcp_server/server.py
    • Requirements: pyproject.toml (or leave blank)
  4. Configure environment variables in the FastMCP Cloud interface:
    Environment VariablePrefect CloudSelf-hosted Prefect
    PREFECT_API_URLhttps://api.prefect.cloud/api/
    accounts/[ACCOUNT_ID]/
    workspaces/[WORKSPACE_ID]    		Your Prefect server URL (e.g., http://your-server:4200/api)
    PREFECT_API_KEYYour Prefect Cloud API keyNot used
    PREFECT_API_AUTH_STRINGNot usedYour authentication string (if using basic auth)
  5. Get your server URL (e.g., https://your-server-name.fastmcp.app/mcp)
When deploying to FastMCP Cloud, you must provide credentials via environment variables since the server has no access to your local Prefect configuration.

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Client setup

Configure your AI assistant to connect to the Prefect MCP server.

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General setup

All MCP clients need three pieces of information to connect to the Prefect MCP server:
  1. Command: uvx
  2. Arguments: --from prefect-mcp prefect-mcp-server
  3. Environment variables (optional): Credentials for your Prefect instance
The configuration format varies by client. Choose your client below for specific setup instructions:
Add the Prefect MCP server to Claude Code using the CLI:
# Minimal setup - inherits from local Prefect profile
claude mcp add prefect -- uvx --from prefect-mcp prefect-mcp-server

# With explicit Prefect Cloud credentials
claude mcp add prefect \
  -e PREFECT_API_URL=https://api.prefect.cloud/api/accounts/[ACCOUNT_ID]/workspaces/[WORKSPACE_ID] \
  -e PREFECT_API_KEY=your-cloud-api-key \
  -- uvx --from prefect-mcp prefect-mcp-server

# With FastMCP Cloud deployment (HTTP transport)
claude mcp add prefect \
  -e PREFECT_API_URL=https://api.prefect.cloud/api/accounts/[ACCOUNT_ID]/workspaces/[WORKSPACE_ID] \
  -e PREFECT_API_KEY=your-cloud-api-key \
  --transport http https://your-server-name.fastmcp.app/mcp
Add the Prefect MCP server to Cursor by creating or editing .cursor/mcp.json in your project:
{
  "mcpServers": {
    "prefect": {
      "command": "uvx",
      "args": ["--from", "prefect-mcp", "prefect-mcp-server"]
    }
  }
}
To use explicit credentials, add an env section:
{
  "mcpServers": {
    "prefect": {
      "command": "uvx",
      "args": ["--from", "prefect-mcp", "prefect-mcp-server"],
      "env": {
        "PREFECT_API_URL": "https://api.prefect.cloud/api/accounts/[ACCOUNT_ID]/workspaces/[WORKSPACE_ID]",
        "PREFECT_API_KEY": "your-cloud-api-key"
      }
    }
  }
}
Add the Prefect MCP server to Codex using the CLI:
# Minimal setup - inherits from local Prefect profile
codex mcp add prefect -- uvx --from prefect-mcp prefect-mcp-server

# With explicit Prefect Cloud credentials
codex mcp add prefect \
  --env PREFECT_API_URL=https://api.prefect.cloud/api/accounts/[ACCOUNT_ID]/workspaces/[WORKSPACE_ID] \
  --env PREFECT_API_KEY=your-cloud-api-key \
  -- uvx --from prefect-mcp prefect-mcp-server
Alternatively, edit ~/.codex/config.toml directly:
[mcp.prefect]
command = "uvx"
args = ["--from", "prefect-mcp", "prefect-mcp-server"]

# For explicit credentials, add:
[mcp.prefect.env]
PREFECT_API_URL = "https://api.prefect.cloud/api/accounts/[ACCOUNT_ID]/workspaces/[WORKSPACE_ID]"
PREFECT_API_KEY = "your-cloud-api-key"

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Credentials configuration

The Prefect MCP server authenticates with your Prefect instance using the same configuration as the Prefect SDK.

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Default behavior

When running locally without environment variables, the server inherits credentials from your active Prefect profile:
  • Profile configuration: ~/.prefect/profiles.toml
  • Uses the same API URL and authentication as your current prefect CLI commands

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Environment variables

Override the default credentials by setting environment variables: For Prefect Cloud: 
PREFECT_API_URL=https://api.prefect.cloud/api/accounts/[ACCOUNT_ID]/workspaces/[WORKSPACE_ID]
PREFECT_API_KEY=your-cloud-api-key
For self-hosted Prefect with basic auth: 
PREFECT_API_URL=http://your-server:4200/api
PREFECT_API_AUTH_STRING=your-auth-string
Find your account ID and workspace ID in your Prefect Cloud browser URL:https://app.prefect.cloud/account/[ACCOUNT-ID]/workspace/[WORKSPACE-ID]/dashboard

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Credential precedence

Environment variables take precedence over profile settings:
  1. Environment variables (PREFECT_API_URL, PREFECT_API_KEY)
  2. Active Prefect profile (~/.prefect/profiles.toml)

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Available capabilities

The Prefect MCP server provides these main capabilities:

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Monitoring & inspection

  • View dashboard overviews with flow run statistics and work pool status
  • Query deployments, flow runs, task runs, and work pools with advanced filtering
  • Retrieve detailed execution logs from flow runs
  • Track events across your workflow ecosystem
  • Review automations and their configurations

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Orchestration & actions

  • Trigger deployment runs with custom parameters and tags
  • Pass dynamic configurations to workflows at runtime

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Intelligent debugging

  • Get contextual guidance for troubleshooting failed flow runs
  • Diagnose deployment issues including concurrency problems
  • Identify root causes of workflow failures
  • Analyze rate limiting issues (Prefect Cloud only)

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Documentation access

The MCP server includes a built-in docs proxy that provides AI assistants with up-to-date information from the Prefect documentation. This enables your AI assistant to:
  • Look up current API syntax and usage patterns
  • Find the correct prefect CLI commands for creating and updating resources
  • Access the latest best practices and examples

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Prompting tips

To get the most out of the Prefect MCP server, guide your AI assistant with these patterns:

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Use the prefect CLI for write operations

The MCP tools are optimized for reading and monitoring. For creating or updating resources, prompt your assistant to use the prefect CLI: Example prompts:
  • “Use the prefect CLI to create a new deployment”
  • “Show me how to update this deployment’s schedule using prefect”
  • “Create an automation using the prefect CLI”

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Leverage the docs proxy

The integrated docs proxy gives your assistant access to current Prefect documentation: Example prompts:
  • “Look up the latest syntax for creating a work pool”
  • “Find documentation on how to configure Docker work pools”
  • “What are the current best practices for deployment configuration?”

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Ask diagnostic questions

The MCP server excels at helping diagnose issues: Example prompts:
  • “Why is my deployment not running?”
  • “Debug the last failed flow run”
  • “Why are my flow runs delayed?”
  • “Show me which work pools have no active workers”

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Learn more

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