Claude Code, Cursor, Codex CLI, Gemini CLI, 또는 MCP 호환 클라이언트의 여러 AI 코딩 에이전트를 전환하는 모델 Context Protocol (MCP) 서버 - 채팅, 디베이트, 기억, 감사 보안 및 같은 프로젝트에서 병렬로 작동합니다.

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개요

대부분의 AI 코딩 조수는 한 번에 한 맨끝을 작동합니다. Claude Team MCP는 여러 에이전트가 측면을 실행하고 실제 소프트웨어 팀과 같은 행동 할 수 있습니다. 프로젝트 관리자는 작업, 백엔드 및 Frontend 엔지니어가 병렬, QA 에이전트 리뷰에 구축하고 공유 채널을 통해 서로 이야기합니다.

간단한 메시징을 넘어, 에이전트는 debate 결정을 내릴 수 있습니다. (propose → critique → revise → 심사위원 결정), persistent project memory, spawn을 공유하고 terminal windows를 닫고, self-contained “second Brain” 지식 그래프를 유지합니다.

모든 것은 하나의 공유 상태 파일을 통해 조정됩니다, 그래서 클라이언트의 번호 - 심지어 다른 CLIs - 같은 파일에 동일한 세계에 포인트.

내장 **MCP 허브 / 게이트웨이 **로 배송됩니다. other 도구에 대한 라우터 / 프록시 역할을하는 서버.

Claude Team MCP

A Model Context Protocol (MCP) server that turns multiple AI coding agents — across Claude Code, Cursor, Codex CLI, Gemini CLI, or any MCP-compatible client — into a coordinated team that chats, debates, remembers, audits security, and works in parallel on the same project.

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Overview

Most AI coding assistants work one terminal at a time. Claude Team MCP lets several agents run side by side and behave like a real software team: a Project Manager hands out tasks, a Backend and Frontend engineer build in parallel, a QA agent reviews — and they talk to each other through a shared channel while it happens.

Beyond simple messaging, agents can debate a decision (propose → critique → revise → a judge decides), share a persistent project memory, spawn and close their own terminal windows, and maintain a self-contained “second brain” knowledge graph on disk.

Everything is coordinated through a single shared state file, so any number of clients — even different CLIs — that point at the same file join the same world.

It also ships with a built-in MCP Hub / Gateway: one server that acts as a router/proxy for other tools. Register downstream MCP servers and REST APIs as “targets”, and any agent connects once to the hub to reach all of them — with credentials held centrally (agents never see the keys), unified tool discovery, per-agent rate limits, a central audit trail, and routing rules. An Auto-Adapter Generator turns any OpenAPI/Swagger spec into a ready-to-run MCP server, so wrapping a new API takes seconds instead of hand-writing an adapter.

Why it exists

A single agent has one perspective and one context window. Real engineering work benefits from division of labor, review, and disagreement. This project provides the missing coordination layer so independent agents can:

• divide work and track it on a shared board,
• communicate in real time without manual copy-paste,
• argue toward the right solution instead of accepting the first idea,
• preserve decisions and knowledge across sessions (saving tokens), and
• scale up or down by opening/closing agent terminals on demand.

Features

• Team coordination — a shared chat channel and task board every agent can see.
• Live conversation — wait_for_message blocks until a new message arrives, so agents converse in real time instead of polling.