Coze MCP Server Overview

The Coze MCP Server is a lightweight Model Context Protocol (MCP) implementation that bridges Claude and other AI assistants with the rich ecosystem of Coze, a collaborative AI platform. It exposes a set of high‑level tools that let developers query and manipulate Coze workspaces, bots, and workflows directly from an AI conversation. This eliminates the need for separate API calls or custom integrations, allowing assistants to act as a unified interface for Coze’s capabilities.

At its core, the server translates MCP tool calls into Coze API requests. For example, a user can ask an assistant to “list my workspaces” or “create a new bot,” and the server will return structured JSON containing workspace IDs, bot configurations, or voice options. Because each tool follows the MCP specification, the assistant can validate arguments, handle errors gracefully, and present results in a conversational manner. This tight coupling makes it straightforward to embed Coze functionality into chat‑based workflows without exposing raw API details.

Key features include:

• Workspace and bot management – list, create, update, retrieve, and publish bots; enumerate workspaces and voices.
• Interactive chat tools – and enable the assistant to initiate or continue conversations with Coze bots or complex workflow chains.
• User context – provides self‑user information, useful for personalizing interactions or enforcing permissions.
• Extensibility – the server can be run via , Docker, or pip, making it adaptable to various deployment environments.

Typical use cases involve building AI‑powered assistants that can orchestrate Coze bots on demand. For instance, a project manager could ask the assistant to “deploy a new marketing bot in workspace X,” and the MCP server would handle all API interactions, returning confirmation within the chat. Similarly, developers can prototype workflow integrations by calling , allowing rapid iteration without leaving the conversational interface.

By integrating Coze’s collaborative features directly into MCP‑enabled assistants, developers gain a powerful tool for automating routine tasks, managing AI assets, and creating seamless human‑AI collaboration pipelines—all while maintaining the simplicity of a chat interface.