"A search for the GitHub repository OrionSmithers/agent-data-pro" matching MCP servers:

Give any LLM agent a real Android or iPhone. 62 MCP tools: tap, swipe, type, screenshot, screen-tree reading, app launch, camera, TTS, crash reports, batched execution. Android via ADB, iPhone via WebDriverAgent, on-device inference, Docker+KVM emulators. Works with Claude Code, Cursor, LangChain, LlamaIndex, and any MCP client. MIT.

Provides agents with real Linux VMs to run commands, manage files, expose ports, and track costs, all through an ephemeral sandbox service.

Enables AI to control Final Cut Pro on macOS via CLI or MCP, allowing automation of tasks like library/project reading, importing FCPXML, and creating title/storyboard projects.

Provides AI agents with environmental sensing capabilities including current time, IP-based geolocation, system information, and hardware details. Enables more contextual and accurate AI responses based on user's environment.

Agent Droid Bridge gives AI agents programmatic control over Android devices and emulators via ADB, exposed as an MCP server.

A local MCP server for trusted NixOS automation that enables inspecting files, applying patch sets, and validating/switching NixOS or Home Manager configurations.

MCP server that lets a private ChatGPT app remotely control a paired Windows PC, including files, PowerShell, programs, screenshots, mouse/keyboard, clipboard, and Chrome.

Enables access to and searching of browser history from major browsers (Brave, Chrome, Firefox, Safari, Edge, Arc, Opera, DuckDuckGo) to personalize LLM interactions and retrieve past browsing data through natural language queries.

A small MCP server that wraps MoneyMoney on macOS via AppleScript, exporting account lists and transactions (bookingDate, amount, name, purpose) as plists for invoice reconciliation, complementing a companion Apple Mail server.

Enables a private ChatGPT app to remotely control a Windows PC through file operations, PowerShell, screenshots, mouse and keyboard input, clipboard, and Chrome browser automation.

Local MCP server for observing and controlling an authorized Android device over USB using ADB and scrcpy, providing screen capture, UI automation, app inspection, logcat, and evidence recording.

A local MCP server that controls Android devices via ADB, enabling AI agents and users to manage devices, execute UI automation, and perform testing through 27 MCP tools and an optional web console.

A high-performance Model Context Protocol (MCP) server built for Windows. Leveraging the native Everything SDK, it provides AI models (like Claude, GPT, Gemini) with millisecond-speed file searching, statistical analysis, and disk usage insights.

An MCP server and CLI for controlling FarmBot hardware, enabling AI agents to manage gardening tasks through tools like gantry movement and device status monitoring. It supports executing Lua scripts and core hardware commands like homing and emergency stops via the Model Context Protocol.

Exposes native iPhone device services and UI automation through MCP, enabling agents to inspect and control trusted iPhones via a unified interface.

Enables AI assistants to capture screenshots and control desktop input (mouse, keyboard) to see and interact with your screen. Features user-first safety controls including automatic pause on user activity and app allowlists to restrict interactions to approved applications only.

This server provides: * Fast file search capabilities using Everything SDK * Windows-specific implementation * Complements existing filesystem servers with specialized search functionality

A black-box desktop automation MCP server that handles screenshots, coordinates, and clicks internally to complete tasks given by the user.

A local evidence recorder that captures screenshots, recordings, notes, and summaries during long-running agent tasks, enabling reviewable task records with timelines and artifacts.

A Windows desktop automation MCP server that enables UI recognition through OCR, UIA controls, and multi-point color matching. It allows agents to interact with desktop applications via actions like clicking and typing while using a learning system to track and improve operation success.