Jetson MCP Server

Jetson‑MCP is a lightweight Model Context Protocol (MCP) server designed to expose the hardware and software state of an NVIDIA Jetson board over a network. By running on the Jetson itself, it lets AI assistants and other MCP‑compatible clients issue natural‑language queries that are translated into concrete system calls, returning structured data back to the caller. The server is built on FastMCP, which simplifies creating SSE‑based endpoints that can be consumed by tools such as Claude, Cursor, or custom dashboards.

The primary problem Jetson‑MCP solves is the lack of a unified, AI‑friendly interface for monitoring and managing Jetson devices in production or research environments. Without such an interface, developers must manually SSH into each board, parse configuration files, or write bespoke scripts to collect metrics. Jetson‑MCP abstracts these details behind two simple tools: and . The former reads to reveal the module or carrier board, while the latter pulls Jetpack version from and kernel information from . These tools provide a consistent JSON output that can be consumed by an AI assistant, enabling queries like “What Jetpack version is running on the device?” or “Show me the current kernel release.”

Key features include:

• SSE Transport: The server publishes its tools over Server‑Sent Events, making it trivial to connect from any MCP client that supports SSE without requiring a persistent socket or complex authentication.
• Systemd Integration: A helper script installs the server as a systemd service, ensuring it starts automatically on boot and can be managed with standard Linux tooling.
• Python Virtual Environment: Dependencies are isolated in a , keeping the Jetson’s system packages clean.
• Extensibility: Because it uses FastMCP, adding new tools or resources is straightforward—just expose another function and register it with the server.

In real‑world scenarios, Jetson‑MCP is valuable for edge AI deployments where multiple boards run concurrently. An operations dashboard can query each board’s status via MCP, aggregate results, and surface alerts to an AI assistant that recommends firmware updates or power‑management tweaks. For developers prototyping on a single Jetson, the server enables rapid iteration: they can ask an assistant to report current GPU utilization or memory usage and receive instant answers without leaving the chat interface. By centralizing hardware introspection behind a standard protocol, Jetson‑MCP turns low‑level system data into actionable intelligence that AI tools can leverage in automation, monitoring, and troubleshooting workflows.