"Best CPUs for working and coding with VSCode" matching MCP servers:

Connects VSCode with MCP to provide AI assistants real-time access to LSP diagnostics, symbol info, and code navigation, enabling efficient code analysis without slow build commands.

Provides disposable email inboxes for AI assistants to sign up, receive emails, and extract verification codes.

A Model Context Protocol (MCP) server for producing better Terraform through CLI analysis (tflint, checkov, trivy, kics, infracost), best-practice guidance from terraform-best-practices.com, cloud provider recommendations (Azure, AWS, GCP), and Terraform Registry resource and module guidance.

Enables AI agents to control VSCode workspaces, including file operations, terminal commands, search, and workspace management.

An MCP bridge that exposes A2A agents as tools in VS Code agent chat, enabling natural language delegation to remote agents with multi-turn conversation and configurable authentication.

Enables AI agents to access VS Code's LSP-based code navigation and native debugging tools (definitions, references, outline, breakpoints, stepping, stack frames, variables, and evaluate) via the MCP protocol.

Enables AI agents to programmatically control and automate VSCode by interacting with its UI, executing commands, and inspecting the DOM structure. It supports advanced workflows like UI testing, extension development, and debugging through a standalone VSCode instance.

Exposes VSCode Jupyter notebooks to MCP-compatible AI agents, enabling them to read, edit, and run cells against the same kernel.

Experimental MCP server for local LLM orchestration with filesystem tools (read, write, list, delete files) and a CLI agent that communicates via Ollama.

A Model Context Protocol (MCP) server that provides VSCode context and filesystem operations for AI assistants.

VS Code Extension & Model Context Protocol Suite Orchestrator for Cursor, Claude Desktop, and VS Code IDEs.

Enables supervising and orchestrating local coding agents like Muse and AGY through a client-agnostic MCP interface, with isolated Git worktrees, safe execution, and deterministic verification.

MCP server to expose VS Code editing features to an LLM for AI coding

Enables LLMs to read and write local user data, generate fake users via sampling, and interact with structured prompts and resources.

Enables delegating coding tasks to multiple AI CLI agents (Claude Code, Antigravity, Codex, Cursor) with session memory and diff inspection.

Exposes VS Code's Debug Adapter Protocol as MCP tools, enabling AI coding agents to set breakpoints, step through code, inspect variables, and read exceptions in a real debug session.

A transport-agnostic MCP server that integrates multiple AI coding agents (Claude Code, Gemini, and Codex) with built-in tools for command execution, calculations, and streaming capabilities. Supports both STDIO and HTTP transports for flexible deployment.

Exposes Jupyter notebook operations as MCP tools over plain HTTP, allowing AI agents to explore, edit, run, and debug notebooks in a live VS Code session using local or remote kernels.

Gives AI agents full control of VS Code's debugging engine — set breakpoints, launch/attach sessions, step through code, inspect call stacks and variables, and evaluate expressions in the paused frame.

Provides a local long-term memory layer for AI coding tools like Cursor and Claude Code, enabling cross-session, cross-tool sharing of project facts, user preferences, decisions, and workflows.