QApilot MCP for Android

QApilot MCP for Android is a Model Context Protocol (MCP) server and CLI that lets developers automate tests on real Android devices and emulators from inside an AI coding client such as Claude, Cursor, or any MCP-compatible AI client. Rather than writing Appium code, users describe a test flow in plain English, and the AI agent builds a structured plan that QApilot executes step by step on the connected device. It is intended for mobile QA engineers, Android developers, and release teams who want Android test automation to run from the tools they already work in. The guide positions QApilot MCP around a simple premise: Android UI automation should not require hand-written Appium code. Test authors describe what they want to verify in ordinary language, and the system handles the mechanics of driving the device. The documentation also points out that step titles are generated automatically with a maximum of 50 characters and no XPath, so reports and the dashboard always stay readable. Before installation, the guide stresses that five prerequisites must be in place: Node.js 18+ (from nodejs.org or nvm), Java JDK 11+ (required by Appium, with JAVA_HOME set in the shell profile), the Android SDK or Studio (which installs adb and platform-tools, with ANDROID_HOME pointing to the SDK path), a USB device with USB debugging enabled or an AVD emulator verified with adb devices, and Appium 2.19.0 installed globally with the UIAutomator2 driver. Installation follows a documented sequence. The CLI is installed globally from the distribution package with a single npm command, then verified by running the server in stdio mode, where the server starts and waits for input without errors. Appium setup is described as a one-time task using pinned versions: Appium 2.19.0 and the UiAutomator2 Android driver 4.2.6, followed by starting the Appium server with the flags shown for chromedriver autodownload, adb shell access, the /wd/hub base path, and CORS, and confirming the device is visible through adb devices. The guide warns that other Appium or driver versions may break the MCP server and recommends keeping Appium running in a separate terminal before starting any test session. Connecting an AI client is done by pasting an MCP configuration block for Claude Desktop, Cursor, or OpenAI Codex, after which QApilot Mobile MCP appears in the client's connected tools list. Restarting the AI client is required after saving the configuration. Once connected, users register and log in through conversation. An account can be created by asking the AI to sign up with an email address; an activation link arrives by email and the login credentials follow. If environment credentials are set in the configuration, the client logs in automatically on first use; otherwise credentials can be supplied in a prompt. Users then create a project to hold their tests and launch their app by giving the Android package ID, with the default project and device selected automatically. From there, test steps are recorded by describing them: the AI builds a structured plan and executes each step on the device in real time. The guide shows example prompts for search flows, login flows with screenshots, navigation and assertions, scrolling and filtering, and form submission. A live preview returns a URL on every app launch call so the device screen can be watched in the browser as steps execute. After a successful run, the recorded steps can be pushed to QApilot as a saved test case for future replay, and only happy-path steps are saved; failures are excluded, and a failed step should be fixed and a passed report generated first. Saved test cases can be replayed one by one, in batches of IDs, or from an Excel sheet such as regression_suite.xlsx, with execution status checked on demand. Every session should end with a report, generated even on failure because it clears execution state so the next test can start cleanly. Reports capture step results, screenshots, errors, and timing, and are written to a dated session folder containing output.json and report.yaml, plus a scenario.feature Gherkin file when the session passes. The server also exposes a cache for XPaths and skills learned per app, with tools to fetch cache context for the current app and summarize what is cached for a given package, along with usage reporting for tokens and API consumption. The overall workflow is agent-led. The AI client lays out the test steps, and QApilot runs them: it finds the elements, waits for the screen to settle, retries when something moves, and caches what it learns so repeat runs are faster. Every passing run saves a Gherkin feature file and becomes a replayable test case, tying prompt-driven testing to a durable regression asset. The MCP server communicates over the Model Context Protocol, exposing dedicated tools for every capability, including account signup and login, project listing and selection, device listing and selection, app listing and launching, session start and stop, plan submission and execution, single manual actions such as tap, type and swipe, execution state, session status and info, preview URLs, accepting steps, replaying test cases, running Excel suites, generating reports, cache inspection, and usage tracking. For users, the benefits described are practical: no Appium code to write, test flows expressed in plain English, execution on real devices and emulators, readable auto-generated step titles, and replayable test cases that accumulate over time. Because caching is retained between runs, repeated executions are faster, and because reports include screenshots, errors and timing, failures are easier to inspect. The preview URL lets teams watch a run from the beginning, and the Excel-driven execution path supports regression batches without rewriting anything by hand. The documented use cases are drawn from everyday mobile app testing. A search flow can be tested by tapping Search, typing a search term such as Honda City, selecting the first result, and verifying the car detail page loads. A login flow can enter an email and password and screenshot the home screen. Navigation and assertions can go to a comparison screen, add two cars, and confirm the compare button is visible. A scroll-and-filter scenario can scroll the filters page, select Petrol as the fuel type, and apply the filter. Form submission scenarios can fill an enquiry form with a name, phone number and city, then submit. Saved suites can then be replayed individually, in sequence, or in bulk from an Excel regression sheet, with status checks in between. QApilot MCP for Android is aimed at mobile QA engineers, Android developers, and teams that already work inside AI coding agents such as Claude, Cursor, or OpenAI Codex, as well as any MCP-compatible client. It integrates with Appium 2.19.0 and the UiAutomator2 driver, the Android SDK and adb, and runs on Node.js 18+ with Java JDK 11+. A Slack community is available for questions, prompts, and setup help. The guide does not state pricing or plan details. In short, QApilot MCP for Android turns an AI coding agent into an Android test automation driver: describe the flow in plain English, let the agent plan it, and let QApilot execute, preview, accept, replay and report on it, with caching that makes the next run faster.