"Finding people on LinkedIn by name" matching MCP servers:

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.

Connects ChatGPT to local files, terminals, and the desktop through MCP with Chrome integration, letting users read and edit project files, run commands, and follow tool results as they arrive. It also adds durable multi-agent workers, Goal/Loop task tracking, mid-task corrections, and Compact & Resume for carrying session and worker history into a fresh chat.

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.

An interactive coding agent and MCP server that provides access to various AI models via the Tsinghua University lab proxy API. It enables users to inspect files and execute shell commands within their local directory using models like DeepSeek, GLM, and Qwen.

AST-targeted code editing MCP server with 28 surgical tools for structural edits across 11 languages. Built on tree-sitter, replaces brittle search/replace with byte-correct edits keyed by symbol names.

Enables multiple AI agents to collaborate on the same git repository by coordinating work via a shared claims branch, detecting file conflicts before they happen.

YOUR AGENTS COLLIDE. File a FlightPlan. Run multiple coding agents without them stepping on each other. FlightPlan gives each agent the same preflight picture, lets them coordinate while plans are still cheap to change, and leaves behind what changed and why for whichever agent comes next. Across sessions. Across people. Across providers. Across time. One shared picture.

Enables multiple AI agents to coordinate work on the same codebase by providing real-time file locking, commit approval, and agent awareness through a lightweight WebSocket-based MCP server.

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.

Enables Claude Code and OpenAI Codex CLI to act as bidirectional partners by letting each ask the other for code review, explanations, planning, performance analysis, and implementation help. It also provides background job tools for starting, observing, waiting on, and cancelling delegated tasks.

Enables AI coding agents on different machines and accounts to work one repository as a team by declaring file and symbol claims before editing, so collisions are refused up front rather than discovered at merge. It also supports delegating tasks between teammates' agents, which do the work with their own tokens and commit, with replies, commit shas, claims and messages landing on one shared board.

Enables ChatGPT to work directly on local projects by reading files, editing code, running tests and commands, inspecting Git state, and managing durable workflows, with optional delegation to local Codex agents for complex tasks.

Enables finding existing shadcn-format UI components by plain-language description instead of exact names, returning ranked matches with install commands and per-dimension match reasons.

Enables multiple AI coding agents to collaborate on a project by coordinating tasks, file leases, and messages through a shared hub, preventing conflicts and enabling parallel development.

Vyuha is an MCP server that enables coding agents to collaborate on a repository by providing tools for ticket creation, worktree management, code review, and staged deployment, all from local files.

Enables MCP clients such as OpenCode, Claude Code, and Codex to control a real VS Code editor by opening files, visibly typing code, running terminal commands, reading diagnostics, and fixing errors through the VS Code API. It makes AI programming observable by applying real progressive edits and actions that are visible on screen.

A token-efficient, codebase-aware AI agent runtime that provides MCP tools for starting runs, getting run states, explaining code, and finding files.

Provides file caching and diff tracking for AI coding agents, reducing token usage by returning changes or confirming no changes instead of full file contents on repeated reads.

Enables web AI agents to operate local projects through MCP, with real-time display of the actual model name and thinking effort.