RabbitMQ MCP Server

The RabbitMQ MCP Server bridges the gap between message‑queue operations and AI assistants by exposing the full breadth of RabbitMQ’s administrative and messaging APIs as Model Context Protocol tools. Rather than having developers manually craft HTTP requests or use command‑line utilities, the server translates high‑level tool calls from an AI client into concrete actions on a RabbitMQ broker. This means an assistant can, for example, create queues, publish messages, or retrieve queue statistics—all within the same conversational flow that it uses to answer user queries.

At its core, the server wraps RabbitMQ’s REST‑based management API and leverages the Pika library for direct AMQP interactions. Developers can point the MCP at any broker instance, even changing targets mid‑conversation, by simply specifying connection parameters in a tool call. The server also supports streamable HTTP responses and integrates with FastMCP’s , allowing secure, token‑based access that can be tied to an external identity provider. This makes it straightforward to expose the server behind existing authentication infrastructures without additional coding.

Key capabilities include:

• Full admin API coverage – Create, delete, and inspect exchanges, queues, bindings, and policies through MCP tools that mirror RabbitMQ’s native endpoints.
• Message‑level operations – Publish, consume, and inspect messages in real time using Pika, giving assistants the ability to manipulate queue contents directly.
• Dynamic broker selection – Specify a different RabbitMQ host, port, or credentials on the fly, enabling multi‑tenant or environment‑specific workflows.
• Secure, streamable communication – Built‑in bearer token support and streaming responses allow large payloads (e.g., message bodies) to be handled efficiently.
• Easy integration – The server is available on PyPI and can be launched via uvx or uv, so developers need not fork the code; simply add a server entry to their MCP client configuration.

Real‑world scenarios where this MCP shines include:

• DevOps automation – An AI assistant can diagnose queue health, adjust prefetch limits, or rotate credentials without manual SSH sessions.
• Event‑driven application debugging – Developers can ask the assistant to inspect a specific message or replay events to reproduce bugs in a conversational manner.
• Multi‑tenant SaaS platforms – A single assistant can manage queues across isolated RabbitMQ instances for different customers, switching contexts as needed.
• Hybrid cloud migrations – While migrating services, the assistant can orchestrate data transfer between source and target brokers, monitoring progress in real time.

By turning RabbitMQ operations into conversational tools, the MCP Server empowers developers to embed sophisticated messaging logic directly into AI workflows. It eliminates boilerplate code, centralizes authentication, and provides a unified interface that scales from local development environments to production clusters—all while maintaining the simplicity of Model Context Protocol interactions.