"Information about Xcode development environment" matching MCP servers:

Unified MCP server for Xcode + iOS Simulator — build, deploy, screenshot, and interact with your iOS app from Claude Code, Cursor, or any MCP client. Built in Swift. Single binary. No Node/Python runtime required.

A Model Context Protocol server that enables AI assistants to build and test Xcode projects directly through a standardized interface, with capabilities for running tests, monitoring progress, and accessing logs in real-time.

A comprehensive MCP server for iOS/macOS development that gives AI assistants full control over Xcode build, test, simulator management, and app deployment.

Connects AI coding clients like OpenCode, Codex, and Claude Code to Xcode and Apple development tools, enabling builds, tests, simulator and device management, profiling, asset handling, and more through natural language.

Enables LLM tools to control iOS Simulator, manage Expo/Metro development servers, capture screenshots and videos, stream logs, and execute UI automation tests via Detox for React Native/Expo applications.

A Model Context Protocol server for JavaScript/TypeScript development, providing tools to run scripts, tests, builds, and dependency management with framework and package manager detection.

An MCP server that provides comprehensive tools for managing iOS simulators, including device control, app lifecycle management, and UI automation. It enables developers to boot devices, install apps, capture screenshots, and simulate user interactions through natural language commands.

Enables reproducible evaluation of AI coding agents by exposing repository inspection, code editing, test running, and deterministic verification through MCP tools.

A testing environment for MCP server setup and client-server interactions, allowing users to verify basic MCP server functionality and test client-server communications including shell command execution.

Enables AI coding assistants to scan an iOS, Android, Flutter, React Native or Expo project for App Store and Google Play rejection risks, returning deterministic findings that cite the guideline section, link to the official Apple or Google page and explain how to fix each problem. Nothing leaves the machine and no AI is used in the checks.

Enables iOS development automation for Claude Code through modular workflow-specific MCP tools for building, testing, launching, and interacting with Xcode, Simulator, and IDB while minimizing token and context usage.

Enables recurring website maintenance checks — domain registration, SSL certificates, uptime, technical SEO and accessibility — across a portfolio of client sites, using publicly available information. Results are returned as structured data and can be sorted by urgency into a client-ready report.

Enables coding agents to compile supplied files, run optional focused tests, and get static findings across ten programming languages, providing a deterministic feedback loop on the code they are about to return.

Exposes MAID Runner validation tools (validate, snapshot, test) as MCP tools, resources, and prompts for AI agents like Claude Code and Aider, enabling seamless integration with AI development workflows.

Exposes six evidence-oriented tools over stdio so MCP clients can navigate unfamiliar code, ask typed questions about repository evidence, review completed changes, and select existing tests.

Umbra is the trust layer for AI-generated code. It scores any repo 0-100 with file:line evidence (security, slop, Docker-verified build/boot, and claim receipts that catch agents lying about tests), and guards agent file writes mid-stream blocking leaked keys, alg:none JWTs, and git-hook plants before they land. Tools: scan_repo, guard_content, get_score.

Automated QA for .NET microservice backends and React frontends, enabling API contract testing, UI crawling, YAML flows, and run diffing against a stage environment.

Connects AI agents to Minecraft servers via RCON to execute commands, monitor logs, and perform read-only SQLite database queries. It is specifically designed to facilitate AI-assisted plugin development, live debugging, and automated testing workflows.

Enables managing TAPD (Tencent Agile Product Development) bugs, requirements, PRD reviews, and test reports directly from IDE via natural language, with cross-project aggregation and built-in workflows.

Wraps Claude Code's code-review skill so any MCP client can run reviews over a PR, branch, working directory, or last commit, with five parallel reviewer subagents, low-to-max effort levels, and optional fix mode that edits files in place. Also supports follow-up questions on a previous review session and a free environment self-check.

Automates visual inspection of development servers by converting natural language test sheets into Playwright scripts and executing them repeatedly.

Enables AI assistants to interact with iOS Simulators and Android Emulators, allowing autonomous app development, UI interaction, profiling, and debugging through natural language.

Enables AI agents to run Jest/TypeScript tests in isolated workspaces for Test-Driven Development workflows.

Enables coding agents to drive and inspect running Shiny apps (R and Python) by setting inputs, clicking, reading outputs, and getting detailed error information.

Automated Playwright E2E test repair powered by a self-improving, governed MCP server that runs failing tests, collects failure artifacts, reasons about root causes, validates and applies fixes, and re-runs to verify.

Enables LLM agents to run self-healing JMeter performance tests via natural language, from environment setup to capacity discovery and SLO verdict reporting.

Enables LLMs to automatically diagnose coding errors through codebase search, test execution, and live debugger integration (DAP/V8 CDP). Provides a secure, policy-gated environment for investigating failures while preventing destructive operations.

An MCP server that turns Zod schemas into mocks, violations, and contract tests, enabling AI agents to reason about API contracts without manually crafting payloads.

Enables AI development environments to call the Diffblue Cover CLI tool to automatically generate unit tests for Java code.

Enables AI assistants to access RenderDoc capture data and assist with graphics debugging by retrieving draw calls, shader information, buffers, textures, and pipeline state.