MCP-ADB

MCP‑ADB Overview

MCP‑ADB is a Model Context Protocol server that bridges AI assistants with Android devices through the Android Debug Bridge (ADB). It solves a common pain point for developers and testers: interacting with physical or emulated Android hardware from within an AI workflow without leaving the assistant’s interface. By exposing device state, screenshots, and input controls as MCP resources and tools, the server lets an AI model inspect, manipulate, and debug an Android device in real time.

The server’s core value lies in its simplicity and tight integration with ADB. Once a device is connected (USB debugging enabled), MCP‑ADB automatically registers each device as a resource (). An AI can query this list, pick a target by its serial number, and then invoke high‑level actions. Two primary tools—screenshot and pressKey—encapsulate the most frequent debugging tasks. The screenshot tool captures a PNG image, resizes it to a width of 640 px for quick display, and returns the data as a base64 string. The pressKey tool sends any Android key event (navigation, back, home, etc.) to the chosen device. This abstraction removes the need for shell commands or manual ADB usage, allowing conversational agents to “look” at a screen or simulate button presses as part of their reasoning.

Key capabilities include:

• Automatic resizing of screenshots for efficient bandwidth usage and immediate visual feedback.
• Base64 image delivery, enabling the AI to embed images directly in responses without external hosting.
• Multi‑device awareness: tools accept an optional so that the assistant can target a specific emulator or phone when several are attached.
• Resource URIs () that expose captured images and device lists, letting the assistant cache or reference them later.

Real‑world scenarios that benefit from MCP‑ADB are plentiful. QA engineers can ask an AI to “show me the current home screen on device emulator‑5554” and receive a live snapshot. Mobile developers can request the AI to “navigate back twice on device 1234” and then verify the resulting UI. Test automation pipelines can embed MCP‑ADB calls in a larger conversational workflow, letting an AI orchestrate complex interaction sequences and report results directly. Because the server exposes standard MCP endpoints, it fits seamlessly into existing Claude Desktop or other AI‑assistant setups, requiring only a configuration entry and the ADB binary.

In short, MCP‑ADB turns any connected Android device into a first‑class AI data source and actuator. It removes manual tooling overhead, offers a clean, declarative API for visual inspection and input simulation, and unlocks powerful debugging and testing workflows that can be driven entirely by natural language.