"iOS Anti-Debugging Techniques and Implementation" matching MCP servers:

Enables AI agents to drive an iPhone or iOS Simulator through 30 tools and 4 resources, using an observe/act/verify loop with built-in safety approvals.

MCP server that exposes GDB debugging as tools. An AI assistant can set breakpoints, run programs, step through code, inspect variables and memory, and examine registers — all via structured tool calls. Reverse debugging with rr is also supported.

MCP server for iOS reverse engineering, enabling static analysis of IPA/Mach-O binaries and dynamic instrumentation via Frida for tasks like IAP interception and FairPlay decryption.

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

A minimal MCP server with a sample search_news skill that demonstrates how Claude Code can automatically call a skill over the MCP protocol.

Controls a running Chrome/Chromium browser via the Chrome DevTools Protocol, enabling navigation, JavaScript evaluation, tab management, and raw CDP commands through MCP tools.

Enables Flutter developers on Windows or Linux to build, sign, test, and ship iOS apps without a Mac, using GitHub Actions macOS runners, App Store Connect API, remote Macs, and USB-connected iPhones.

MCP server for capturing iOS device screenshots using Appium, compatible with AI clients like Trae.

Enables a macOS MCP client to control a real iPhone through CoreDevice services, including screenshots, normalized taps and swipes, text entry, hardware-button presses, and app launching or listing.

Enables comprehensive control of iOS simulators and real devices through AI assistants, supporting app management, UI automation, screenshots, media operations, and location simulation for iOS development and testing workflows.

Enables AI assistants to query the FoxData API for iOS and Google Play app analytics, including downloads, revenue, rankings, keywords, competitor insights, and ad data across 200+ countries. Supports natural-language requests to retrieve app intelligence and market data.

Relays a result screenshot to Slack so you can review a coding agent's output without opening the app.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Enables autonomous web browsing and extraction by canonicalizing URLs, detecting anti-crawler traps such as circular paths and infinite pagination, pruning DOM content, mapping forms, converting tables to JSON, and evaluating agent trajectories.

Serves plain-text notes that an Obsidian user has explicitly published for AI over streamable HTTP, with full-text and embedding-based search. MCP clients can read and search those notes and queue suggested edits that the user's own client applies only if the AI rules allow it.

Provides a hand-written MCP server integrated with a LangGraph agent to benchmark multiple agent topologies and measure their failure rates.

Enables AI assistants to interact with Databricks workspaces, running SQL queries, managing jobs, and exploring schemas via the Model Context Protocol.

Enables personal AI agents and users to reinforce habits, triage decisions, and prevent streak-break abandonment by generating adaptive micro-fallbacks and elastic recommendations.

Enables autonomous web agents to canonicalize URLs, detect anti-crawler traps such as circular paths and infinite pagination, prune DOM tokens, auto-map forms, transform tables into JSON, and evaluate action trajectories through MCP.

Enables personal agents to track habits and receive adaptive micro-fallbacks that degrade gracefully when a streak is missed, rather than letting the user abandon it. Surfaces prioritized queues and elastic recommendations through MCP-compatible clients like Claude Desktop, Cursor, and Windsurf.