AWS MQ MCP Server

AWS MQ MCP Server

AWS MQ MCP Server is an MCP-compatible server that provides AI assistants and applications with the ability to analyze, manage, and provision Amazon MQ resources—including ActiveMQ and RabbitMQ brokers—seamlessly within an AI-native workflow.

Author: awslabs


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What is AWS MQ MCP Server?

AWS MQ MCP Server is an integration server that exposes Amazon MQ operations via the Model Context Protocol (MCP). It enables language models and AI agents to interact directly with Amazon MQ for ActiveMQ and RabbitMQ brokers. Users can programmatically analyze existing brokers and provision new broker instances through a standardized interface, making cloud message broker management accessible and efficient within AI-assisted cloud development workflows.

How to Configure AWS MQ MCP Server

  1. Prerequisites:

    • Ensure you have Python 3.10+ installed (recommended to use uv).
    • Configure your AWS credentials with permissions to manage Amazon MQ resources.

  2. Install the Server (using uvx):

    • Add the AWS MQ MCP server to your MCP client configuration: 
      {
  "mcpServers": {
    "awslabs.amazon-mq-mcp-server": {
      "command": "uvx",
      "args": ["awslabs.amazon-mq-mcp-server@latest"],
      "env": {
        "AWS_PROFILE": "your-aws-profile",
        "AWS_REGION": "us-east-1",
        "FASTMCP_LOG_LEVEL": "ERROR"
      }
    }
  }
}

  3. Alternative Deployment:

    • Optionally, use Docker for containerized deployment and pass AWS credentials and region via environment variables or an .env file.

  4. Enable the Server:

    • Make sure the server is enabled in your AI assistant’s MCP configuration (e.g., within Cline, Cursor, or Windsurf).

How to Use AWS MQ MCP Server

  1. Ensure that the MCP server is running and properly configured within your code assistant or AI workspace.
  2. In your AI chat interface or IDE, prompt your assistant to utilize the AWS MQ MCP Server. For example:
    • “Using the AWS MQ MCP Server, list all existing MQ brokers.”
    • “Provision a new Amazon MQ RabbitMQ broker instance with default settings.”
  3. Approve tool usage if prompted, and review the results or actions taken by the AI assistant.
  4. Use the AI-driven workflow to further analyze, update, or create MQ brokers as needed.

Key Features

  • Analyze existing Amazon MQ brokers for both ActiveMQ and RabbitMQ engines.
  • Provision new Amazon MQ for ActiveMQ and RabbitMQ broker instances programmatically.
  • Natural language interface for broker management via integrated AI assistants.
  • Integrates seamlessly into MCP client environments, supporting secure operations through AWS credentials.
  • Automated resource discovery and contextual recommendations for MQ broker configuration.
  • Consistent, up-to-date interactions with the latest AWS MQ APIs and best practices.

Use Cases

  • Automated Cloud Resource Provisioning: Rapidly set up and configure new message broker infrastructure directly from an AI interface.
  • Contextual Broker Analysis: Request detailed information or status on existing MQ brokers for monitoring or troubleshooting.
  • DevOps Automation: Integrate broker management into broader infrastructure-as-code or AI-driven cloud workflows within VS Code, Cursor, or chat-based assistants.
  • AI-Assisted Migration Planning: Assess and plan migrations between ActiveMQ and RabbitMQ brokers by analyzing broker configurations conversationally.
  • Hands-off Resource Management: Enable non-expert users to manage MQ brokers securely through guided, conversational commands with enforceable approval workflows.

FAQ

Q1: What types of brokers are supported by the AWS MQ MCP Server?
A1: The server supports both Amazon MQ for ActiveMQ and Amazon MQ for RabbitMQ broker engines, allowing you to analyze and manage either type.

Q2: Do I need special IAM permissions to use the server?
A2: Yes, you must configure your AWS credentials with permissions to create, describe, and manage Amazon MQ broker resources.

Q3: Is it possible to provision brokers with custom configurations?
A3: Yes, you can specify custom broker configurations, such as instance type, storage size, and network settings, as part of the broker provisioning process.

Q4: Can I use the server with multiple AWS regions?
A4: Yes, by setting the AWS_REGION environment variable or adjusting your client configuration, you can operate on brokers in any supported AWS region.

Q5: How does the server handle sensitive credentials?
A5: The server uses your locally configured AWS credentials (via profile or environment variables). Ensure you follow best practices for credential storage and scope.