RabbitMQ MCP Server

What does “RabbitMQ” MCP Server do?

The RabbitMQ MCP Server is a Model Context Protocol (MCP) server implementation designed to enable AI assistants to manage and interact with RabbitMQ message brokers. By wrapping the admin APIs of a RabbitMQ broker as MCP tools and utilizing the Pika library for message-level interactions, this server allows AI agents to perform tasks such as managing queues, sending and receiving messages, and monitoring broker status. The RabbitMQ MCP Server supports seamless integration with MCP clients, provides streamable HTTP with FastMCP’s BearerAuthProvider, and allows users to connect to different RabbitMQ brokers during a conversation. It streamlines development workflows by empowering AI agents to automate message queue operations, making it easier for developers to build and manage robust distributed systems.

List of Prompts

No documented prompt templates found in the repository.

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List of Resources

No explicit resource definitions found in the repository.

List of Tools

• Admin API Wrappers: Exposes RabbitMQ administrative APIs as MCP tools, letting AI clients perform broker management tasks.
• Pika-based Message Operations: Uses the Pika library to interact with RabbitMQ at the message level, enabling queue/message creation, consumption, and deletion.
• Broker Switching Tool: Allows specification of a different RabbitMQ broker mid-conversation for dynamic context switching.
  (Descriptions inferred from the README; explicit tool function names are not listed in server.py.)

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Use Cases of this MCP Server

• Automated Queue Management: Developers can use AI agents to create, delete, or configure message queues programmatically, streamlining infrastructure management.
• Message Monitoring and Consumption: AI assistants can monitor queue status, consume messages, and provide real-time analytics or alerts, improving observability.
• Broker Administration: Routine administrative operations like user management, permission setting, and broker health checks can be automated via MCP tools.
• Dynamic Broker Switching: During multi-environment workflows (e.g., staging to production), AI agents can switch RabbitMQ endpoints dynamically without redeployment.
• Integration Testing: Developers can script automated tests for distributed applications by simulating message flows and verifying queue states via AI-driven MCP actions.

How to set it up

Windsurf

1. Ensure Node.js and uvx are installed on your system.
2. Open the Windsurf configuration file.
3. Add the RabbitMQ MCP Server to the mcpServers configuration.
4. Save the changes and restart Windsurf.
5. Verify connection by checking the MCP server logs and Windsurf interface.

JSON Example:

{
  "mcpServers": {
    "rabbitmq": {
      "command": "uvx",
      "args": [
        "mcp-server-rabbitmq@latest",
        "--rabbitmq-host", "<hostname>",
        "--port", "<port number>",
        "--username", "<rabbitmq username>",
        "--password", "<rabbitmq password>",
        "--use-tls", "<true|false>"
      ]
    }
  }
}

Securing API Keys (Environment Variables Example):

{
  "env": {
    "RABBITMQ_USERNAME": "<rabbitmq username>",
    "RABBITMQ_PASSWORD": "<rabbitmq password>"
  },
  "inputs": {
    "username": "${RABBITMQ_USERNAME}",
    "password": "${RABBITMQ_PASSWORD}"
  }
}

Claude

1. Install uvx and ensure Claude is up to date.
2. Open the Claude configuration file.
3. Insert the RabbitMQ MCP Server block into the mcpServers section.
4. Save the file and restart Claude.
5. Confirm setup by sending a test command to the RabbitMQ MCP Server.

JSON Example:

{
  "mcpServers": {
    "rabbitmq": {
      "command": "uvx",
      "args": [
        "mcp-server-rabbitmq@latest",
        "--rabbitmq-host", "<hostname>",
        "--port", "<port number>",
        "--username", "<rabbitmq username>",
        "--password", "<rabbitmq password>",
        "--use-tls", "<true|false>"
      ]
    }
  }
}

Refer to the environment variable example above for securing credentials.

Cursor

1. Install the latest version of Cursor and ensure uvx is available.
2. Locate Cursor’s configuration file.
3. Add the RabbitMQ MCP Server entry to mcpServers.
4. Save the configuration and relaunch Cursor.
5. Test integration by initiating an MCP command.

JSON Example:

{
  "mcpServers": {
    "rabbitmq": {
      "command": "uvx",
      "args": [
        "mcp-server-rabbitmq@latest",
        "--rabbitmq-host", "<hostname>",
        "--port", "<port number>",
        "--username", "<rabbitmq username>",
        "--password", "<rabbitmq password>",
        "--use-tls", "<true|false>"
      ]
    }
  }
}

Use environment variables as shown previously to secure sensitive info.

Cline

1. Ensure Cline and uvx are installed.
2. Edit the Cline configuration file.
3. Register the RabbitMQ MCP Server under mcpServers.
4. Restart Cline to apply changes.
5. Check operation by connecting to the RabbitMQ MCP Server.

JSON Example:

{
  "mcpServers": {
    "rabbitmq": {
      "command": "uvx",
      "args": [
        "mcp-server-rabbitmq@latest",
        "--rabbitmq-host", "<hostname>",
        "--port", "<port number>",
        "--username", "<rabbitmq username>",
        "--password", "<rabbitmq password>",
        "--use-tls", "<true|false>"
      ]
    }
  }
}

Include environment variable configuration as described above.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "rabbitmq": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “rabbitmq” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on the above, RabbitMQ MCP Server offers solid integration and setup documentation, with emphasis on tool usage and security. However, it lacks explicit prompt templates and resource definitions in the public documentation. Roots and sampling support are not documented.

MCP Score

Rating:
I would rate this MCP server a 7/10. It is well-documented and functional for tool-based RabbitMQ integration, but could improve by providing explicit prompt templates, resource definitions, and documented support for Roots and Sampling.