"Understanding Frontend and Backend Development" matching MCP servers:

Enables AI tools to interact with browsers for enhanced frontend development, providing context to LLMs through tools like API call analysis, screenshots, element selection, and documentation ingestion.

An MCP server implementing a 7-stage agentic frontend workflow—from design audit to PR review—including AI-driven component generation, browser validation, E2E testing, and CI self-healing.

Frontend Design Loop MCP enables coding agents to improve web page designs through screenshot-grounded iteration and proof artifacts, making pages materially better.

A specialized toolchain that guides AI agents through a structured 'Atomic Development' workflow for building Python FastAPI and Supabase backends. It manages project scaffolding and enforces dependency-ordered generation of database models, API routes, and tests.

An AI-powered development toolkit for Cursor providing intelligent coding assistance through advanced reasoning, UI screenshot analysis, and code review tools.

An MCP server that enables AI-powered IDEs to implement a structured development workflow from requirements gathering to code implementation, guiding users through goal collection, requirements specification, design documentation, task planning, and execution.

A Cursor-compatible toolkit that provides intelligent coding assistance through custom AI tools for code architecture planning, screenshot analysis, code review, and file reading capabilities.

Enables structured, step-by-step problem-solving through dynamic thinking processes that can be revised, branched, and adjusted as understanding deepens. Supports breaking down complex problems into manageable steps with the ability to revise previous thoughts and explore alternative reasoning paths.

Provides brutally honest code reviews, explanations, and refactoring suggestions via CLI and MCP tools.

A generic OpenAI-compatible subagent MCP server that lets Claude Code delegate coding grunt-work to a local model via a tool-calling agent loop, with configurable concurrency and backend.

Provides AI coding agents with five intelligence layers (dependency graph, git history, documentation, architectural decisions, code health) via nine MCP tools, enabling deep codebase understanding and reducing exploration cost.

Provides AI coding agents a full abap2UI5 development loop—writing ABAP classes, validating views, deploying, and running apps headless with screenshots—all locally without an SAP system.

An MCP server that turns Grok CLI into the execution agent for Claude Code, implementing an orchestrated execute-verify-autofix loop for autonomous development tasks.

MCP server that analyzes videos locally with frame-by-frame understanding and optional browser sidecars, producing structured context bundles for coding agents to fix bugs, add features, or build new functionality.

A safe, local MCP server that lets Claude drive a controlled software-development loop (inspect, read, plan, patch, apply, check, analyze, fix, summarize) on a project, using deterministic tools and real diffs/test runs.

An AI-powered toolkit for the M8 mobile development framework that enables automated generation of Vue pages, low-code components, and mock data. It provides real-time code standard validation and a comprehensive UI component knowledge base to streamline development in AI editors.

Enables interaction with Google Jules API to automate software development tasks, such as managing sources, sessions, and activities for AI-powered coding assistance.

MCP server that provides AI coding agents automatic access to AGENTS.md documentation from GitHub repositories, enabling understanding of codebase conventions and patterns.

Enables Cursor's main agent to delegate development tasks to Claude CLI subagents via MCP, supporting synchronous, asynchronous, and parallel task execution with monitoring and control.

Workspace-aware MCP server for the yudao/ruoyi-vue-pro ecosystem that helps AI coding tools identify backend, frontend, and database configurations so generated code lands in the right project structure.