What components make up the InBrowserMCP Project?
The project has three main parts: a React frontend (port 5173), a Node.js MCP server (port 3000), and a Chrome browser extension (WebSocket port 8081).
@LofiSu
基本信息
express框架搭建的mcp-sdk实例
工具列表暂未提供
当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
The InBrowserMCP Project implements a system based on the Model Context Protocol (MCP) that allows an AI model to interact with a web browser via a browser extension. It consists of a React frontend, a Node.js MCP server, and a Chrome browser extension that communicate to execute browser actions.
Start the MCP server (cd mcp-server && pnpm dev), load the Browser Extension in Chrome (chrome://extensions/), start the Frontend (cd frontend && pnpm dev), then open the frontend application at http://localhost:5173 to send commands that control the browser.
The project has three main parts: a React frontend (port 5173), a Node.js MCP server (port 3000), and a Chrome browser extension (WebSocket port 8081).
The frontend sends user commands to the MCP server, which translates them into MCP tool calls. The server sends browser actions to the extension via WebSocket, and the extension executes them and returns results. The server then streams responses back to the frontend via SSE.
No, an AI model is not explicitly implemented in this project; it is assumed that the frontend or MCP server will interact with an external AI model as part of the system.
You need Node.js and pnpm for the server and frontend, and Google Chrome to load the browser extension. All communication uses localhost ports 5173, 3000, and 8081.
Follow the general workflow: start the MCP server, load the extension in Chrome, start the frontend, and open the frontend URL. Detailed instructions are in the respective README files for each component.

Public read-only MCP server for discovering, inspecting, installing, and sharing approved Codex-compatible animated pet packs.


Verified distributed compute for humans, applications, and agents. Submit Blender, Octane, and video workloads, route them across qualified GPUs, track asynchronous execution, download finished artifacts, and verify FARP

Turn Perfex CRM into an AI-ready workspace. This MCP server exposes your full REST API to Claude, ChatGPT and any AI agent, so leads, invoices and tasks are one prompt away.

ArtiBot Cloud MCP (Zhijiangyun 智匠云) — cloud MCP server for robot manipulation data & GPU workloads. 15 tools: robot fleet management, data collection & video recording, post-processing pipelines, and benchmark evaluation
评论