"Tools and Techniques for Debugging UI in Web Development" matching MCP servers:

Persistent memory and pattern learning for AI coding assistants via MCP.

Facilitates spec-driven development workflows by providing structured prompts for generating requirements in EARS format, design documents, and implementation code following a systematic approach.

An MCP server that provides a persistent sandbox for AI coding agents to explore codebases server-side, returning only compact summaries to reduce context consumption.

Enables Large Language Models to access and manipulate Autodesk Revit models through a pyRevit-based bridge and the Model Context Protocol.

A dynamic MCP server that automatically discovers Python files in a directory and exposes them as tools to any MCP-compatible AI client, allowing users to easily create and deploy custom AI tools.

Enables coding agents to perform file, search, patch, git, process, test, package, network, and system operations through 60 typed MCP tools with structured inputs/outputs, structured errors, and a full event journal, replacing terminal use with a typed machine API.

Enables local code execution via Codex (Luna) through MCP, using ChatGPT Web conversations as a planner. Provides file, terminal, and code execution tools while keeping long-running jobs manageable.

Enable Claude (or any other LLM) to interactively debug your code (set breakpoints and evaluate expressions in stack frame). It's language-agnostic, assuming debugger console support and valid launch.json for debugging in VSCode.

Enables step-through debugging for C#, Node.js/TypeScript, Python, and Dart applications on Windows through a unified MCP interface. Acts as a bridge between MCP clients and various debug adapters, providing consistent debugging workflows with breakpoints, variable inspection, and process attachment capabilities.

All In One Model Context Protocol: qdrant, google suite (gmail, calendar, ...), jira, gitlab, CLI, ...

Provides access to OpenAI's advanced models (including o3) with integrated web search, Python code interpreter, and combined analysis capabilities. Enables users to perform web searches, execute code in sandboxed environments, and combine search with analysis through natural language.

A Model Context Protocol (MCP) server that provides TypeScript development tools for automated refactoring and code analysis.

Provides a Docker-based development environment with Python and Node.js MCP servers, enabling file operations, SQL queries, Redis caching, and debugging for building and testing MCP servers.

Extends VS Code Copilot with MCP tools for persistent semantic memory, code indexing and RAG search, sandboxed Python/shell execution, web fetch/search, dynamic tool management via a web UI, and structured multi-step reasoning.

A collection of production-ready MCP tools for common tasks such as web search, code execution, file operations, and database queries, enabling AI agents to perform these actions through a unified interface.

Enables AI assistants to perform controlled Linux system administration tasks like reading logs, managing services, cron jobs, WordPress, and executing sandboxed Python code, with strict security constraints.

Guides problem-solving by breaking down complex problems into steps and recommending appropriate MCP tools for each stage, with confidence scores and rationales for tool suggestions.

MCP server wrapping DeepSeek's agent harness tools into minimal/standard modes, enabling persistent shell, file editing, search, job management, and more via natural language.

Enables dynamic creation and execution of custom tools/functions in multiple programming languages at runtime, exposing them to MCP clients like Claude.

A Model Context Protocol server for time manipulation tasks, enabling AI models to get the current date/time and calculate duration between timestamps.