"Iterating on UI designs and improving prompts" matching MCP servers:

Provides coding agents live access to shadcn-style component registries, enabling them to search, compare, and fetch real component source code for UI composition.

Search, inspect and install Ballmac UI React + Tailwind components, blocks and templates, with props, keyboard notes and the exact shadcn install command, and plan whole pages from blocks.

Bridges the ChatGPT web UI to a local development environment over MCP, letting the model read and patch files, run real shell commands and terminals, execute tests, and control the desktop (screen, mouse, keyboard, clipboard) within user-approved folders. It adds parallel workers, goal/loop long-task control, plugin tools, and a security-hardening layer with risk-tiered shell policy, audit logging, and credential sanitization.

Enables coding agents to inspect live browser runtime evidence, such as blocking elements and interaction surfaces, and to independently verify whether a code fix resolved the issue.

Enables ChatGPT to work on a local project by reading and editing files, running shell commands, keeping terminals and desktop sessions open, and splitting work across durable workers whose context persists between tasks. Long-running work can be steered mid-flight, tracked with Goal, and carried into fresh chats via Compact & Resume.

Lets ChatGPT work directly on a local project by reading and editing files, running shell commands and tests, keeping terminals open, and using the user's desktop, all paired with an approved workspace folder. It also splits independent jobs across persistent workers that retain context, while riding the user's existing ChatGPT plan rather than Codex quota.

Converts screenshots into context-aware UI code that integrates seamlessly with existing Angular projects, considering project structure, conventions, and dependencies.

MCP server that runs Kimi K2.7-code as an autonomous coding agent inside a bubblewrap-sandboxed git worktree, returning git diffs of changes for review.

Turn rough requests into rigorously structured prompts for any coding agent. Quality-scored to ≥90/100 across 12 dimensions, calibrated on 1,000+ real coding cases.

Bridges multiple CLI coding agents (Codex, Cursor, OpenCode, Claude, Antigravity) into any MCP client, enabling delegation of prompts, parallel execution, and code review workflows.

Delegates coding tasks to the locally installed DeepSeek Harness CLI, enabling MCP clients to run prompts through dsh and receive results with exit code and timing.

A secure MCP server that gives AI coding assistants direct access to the shadcn/ui registry, enabling fetching, installing, and wiring up components without switching context.

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

Enables coding agents to build, drive, and debug Android apps on a real device, with screenshots, UI dumps, local vision model analysis, and lock-safe Gradle builds.

Two-way MCP bridge between Claude and Codex: send prompts into a live Codex Desktop thread and messages into a running Claude Code session, keeping each conversation in its own app. Native Desktop tasks on Windows and macOS; CLI/app-server mode on Linux and WSL.

MCP server that lets you drive Claude Code sessions hands-free by voice from any MCP client, enabling remote models to send prompts, monitor progress, and get results read back.

MCP server for Google Jules enabling LLMs to create coding sessions with automatic pull request creation from issues or custom prompts.

Official implementation of Anthropic's 'think' tool that provides Claude with a dedicated space for structured reasoning, improving performance by up to 54% on complex tasks requiring multi-step problem solving.

Provides unified access to multiple CLI AI agents (Codex, Gemini, Claude, and OpenCode) through a single MCP interface with real-time task monitoring, enabling specialized code analysis, UI design, implementation, and prototyping workflows.

MCP server for the FloopFloop API that lets LLM hosts build, poll, and refine projects from natural language prompts.