Hamibot MCP Server

Overview

Hamibot MCP Server is a Model Context Protocol (MCP) implementation that bridges AI assistants with the Hamibot ecosystem. It exposes a set of tools for managing connected devices, orchestrating automation scripts, and executing custom JavaScript code directly on the hardware. By providing a unified MCP interface, developers can incorporate device control and automation into conversational agents without writing low‑level API calls or handling authentication manually.

The server solves the common pain point of integrating IoT devices into AI workflows: developers must usually write bespoke SDK wrappers, manage tokens, and parse HTTP responses. Hamibot MCP Server abstracts these details behind a clean set of commands—, , , and . Each command accepts a minimal set of parameters, such as device identifiers or script IDs, and can pass arbitrary variables to the underlying Hamibot scripts. This design allows a conversational AI to ask for “list all devices” or “run the demo script on my living room robot,” and have those requests translated into authenticated API calls automatically.

Key capabilities include:

• Device inventory: Quickly enumerate every device currently connected to a Hamibot account, enabling dynamic discovery and selection.
• Script lifecycle management: Retrieve available automation scripts and trigger them on one or more devices with optional runtime parameters.
• Remote code execution: Send raw JavaScript snippets to a device, allowing ad‑hoc logic or debugging without uploading new script files.
• Variable passing: Both scripts and code blocks can receive custom variables, making the same MCP tools usable for parameterized workflows.

Real‑world scenarios that benefit from this server are abundant. A home automation assistant can list all smart appliances, then launch a “turn off lights” script on every device tagged with . A maintenance bot can execute diagnostic code on a fleet of robots, collecting telemetry for analysis. In industrial settings, an AI‑powered monitoring system can trigger safety scripts on machines that report abnormal sensor readings. Because the MCP interface is language‑agnostic, any AI platform that supports MCP—such as Claude or OpenAI’s new function calling extensions—can invoke these tools directly from natural language prompts.

Integration into existing AI pipelines is straightforward. After configuring the MCP server in a tool like Trae, developers add a single JSON entry that specifies the command to launch the server and the environment variable holding the Hamibot personal access token. Once registered, AI assistants can call any of the exposed tools by name, passing arguments as JSON objects. The server handles authentication, serializes requests to the Hamibot API, and streams back results in a format that the assistant can render or further process.

What sets Hamibot MCP Server apart is its focus on simplicity and extensibility. By exposing a small, well‑defined set of tools that map directly to Hamibot’s core features, it eliminates boilerplate and reduces the cognitive load on developers. The ability to execute arbitrary JavaScript on devices gives advanced users a powerful sandbox for experimentation, while the built‑in variable passing keeps scripts reusable across contexts. For teams looking to embed device control into conversational AI, Hamibot MCP Server offers a ready‑made bridge that scales from single‑device prototypes to large fleets of connected hardware.