Omni MQTT MCP Server

The Omni‑MQTT‑MCP server bridges the gap between MQTT brokers and AI assistants that rely on the Model Context Protocol (MCP). By exposing a lightweight MCP interface, it lets assistants such as Claude publish to and subscribe from any MQTT topic without needing direct broker access or custom client libraries. This simplifies integration of real‑time messaging, IoT telemetry, and event streams into conversational AI workflows.

At its core, the server accepts MCP requests over three transport layers—STDIO for local development, Streamable HTTP for web‑based or microservice deployments, and the deprecated SSE channel. The choice of transport is controlled via a single CLI flag, allowing developers to spin up a local test instance with the default STDIO or expose a REST‑style endpoint for remote clients. The Streamable HTTP transport is particularly valuable in production, as it supports multiple concurrent MCP clients and can be bound to any host/port configuration, making it ideal for containerized services or edge deployments.

Key capabilities include two built‑in tools: and . These tools translate MCP commands into standard MQTT publish or subscribe operations, handling topic resolution, QoS levels, and message payloads transparently. Developers can also configure the underlying MQTT connection through command‑line arguments or environment variables, specifying broker address, port, client ID, and credentials. This flexibility enables seamless operation across public cloud brokers, private on‑premise clusters, or local test environments.

Real‑world use cases are plentiful. In an IoT scenario, a conversational agent can receive sensor updates in real time by subscribing to MQTT topics and then generate contextual responses or trigger alerts. In a microservice architecture, the server can act as an MCP gateway that forwards user commands from an assistant to downstream services via MQTT, decoupling the AI layer from business logic. For remote monitoring dashboards, developers can expose the Streamable HTTP transport behind a reverse proxy and let web‑based assistants fetch telemetry data without exposing broker credentials.

The server’s design offers unique advantages: it requires no additional dependencies beyond standard Python libraries, supports secure local development out of the box, and can be deployed as a standalone process or integrated into existing MCP workflows using the CLI. Its configurable transport options and straightforward MQTT configuration make it a versatile component for any team looking to enrich AI assistants with real‑time messaging capabilities.