Claudeコード、Cursor、Codex CLI、Gemini CLI、またはMCP互換クライアントの複数のAIコーディングエージェントを回すモデルコンテキストプロトコル(MCP)サーバー(MCP)サーバー。チャット、議論、記憶、監査セキュリティ、および同じプロジェクトで並行して動作します。

お問い合わせ

概要

ほとんどのAIコーディングアシスタントは、一度に1つのターミナルを動作させます。 Claude Team MCPは、複数のエージェントがサイドバイサイドで実行し、プロジェクトマネージャーがタスクを手渡し、バックエンドとフロントエンドエンジニアが並行してビルドし、QAエージェントのレビューを受け取り、共有チャネルを通じて互いに話し合います。

単純なメッセージングを超えて、エージェントは議論の決定(propose → critique → revise → 判断決定)を超えて、永続的なプロジェクトメモリを共有し、独自のターミナルウィンドウをスポーンして閉じ、自己完結秒の脳を維持しますディスク上の知識グラフ。

ひとつの共有ステートファイルですべてを調整するので、どのクライアントも異なるCLIでも同じファイルで同じ世界に入ります。

また、組み込みのMCPハブ/ゲートウェイで出荷されます。otherツールのルータ/プロキシとして機能するサーバーです.

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.