What are the system requirements?
Java 21, Maven, an mBot2 robot with mBlock IDE, an MQTT broker (can be run locally with Docker), and basic Java knowledge.
@deemkeen
Basic information
control your mbot2 with a power combo: mqtt+mcp+llm
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"mbotmcp": {
"command": "path-to-java-executable",
"args": [
"-jar",
"/path-to-repo/mbotmcp/target/mbotmcp-0.0.1-SNAPSHOT.jar"
],
"env": {
"MQTT_USERNAME": "<mqtt server username>",
"MQTT_PASSWORD": "<mqtt server password>",
"MQTT_SERVER_URI": "mqtt server url"
}
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
MBotMcp is a Spring Boot application that uses Spring AI and the Model Context Protocol (MCP) to let AI models control a physical mBot2 robot through natural language commands. It acts as an MCP server, exposing robot control commands as AI-callable tools and relaying them via an MQTT broker to the robot.
First, set up an MQTT broker (e.g., with the included Docker Compose file), configure the required environment variables (MQTT_USERNAME, MQTT_PASSWORD, MQTT_SERVER_URI), and upload the mbot-mqtt.py Python script to your mBot2 after editing its WiFi/MQTT settings. Then build the Spring Boot application with mvn clean package and run the test client with mvn test -Dtest=ClientStdioTest. Finally, connect an LLM client that supports MCP (such as Goose) to the server to send natural language commands to the robot.
@Tool annotationJava 21, Maven, an mBot2 robot with mBlock IDE, an MQTT broker (can be run locally with Docker), and basic Java knowledge.
Set the environment variables `MQTT_USERNAME`, `MQTT_PASSWORD`, and `MQTT_SERVER_URI` in the Spring Boot app. Edit the uploaded `mbot-mqtt.py` script to include your WiFi credentials and MQTT broker details.
Any LLM client that supports the Model Context Protocol (MCP). The README specifically recommends using Goose.
Commands flow from the AI client through the MCP server to the MQTT broker, then to the mBot2 robot. No external cloud storage is used; all data stays on the local MQTT broker.
The author provides a humorous disclaimer: “If your robot starts planning world domination, the author accepts no responsibility. Just unplug it and run! 😂” The robot can be stopped with the `mbotStop()` command or by powering it off.
Hindsight: Agent Memory That Learns
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Model Context Protocol Servers
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