Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP Feedback Enhanced collect feedback on my new UI design with image uploads"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
感谢f的反馈项目，本版本修复了以下问题并新增功能：
超时设置问题：修复了超时时间永远是 600s 的问题，现已修复默认 24 小时超时 ，可以长时间等待反馈啦
图片上传问题：修复了无法上传图片报错序列号错误的问题，现已支持更多类型图 片 😁🎉
断网重连功能：新增断网不断链接功能，现在可以离线同一个会话等待（适合使用 手机热点为电脑提供互联网的场景）
Fork 本项目到本地，在 Cursor 中配置：
{
"mcpServers": {
"mcp-shouji-local": {
"command": "uv",
"args": [
"run",
"--directory",
"C:\\Users\\Administrator\\Desktop\\project\\mcp-shouji-main",
"python",
"-m",
"mcp_shouji"
],
"timeout": 86400,
"env": {
"MCP_DEBUG": "false",
"MCP_WEB_HOST": "127.0.0.1",
"MCP_WEB_PORT": "8765",
"MCP_DESKTOP_MODE": "false",
"MCP_LANGUAGE": "zh-CN"
},
"autoApprove": ["interactive_feedback"]
}
}
}注意：请将
"C:\\Users\\Administrator\\Desktop\\project\\mcp-shouji-main"改 为您本地的项目位置，这样可以实现本地高自由度的定制。
獲取系統環境資訊
Returns: str: JSON 格式的系統資訊
Inputs: no named parameters. Returns: result
通过 Web 界面收集用户输入。
提供一个交互式界面，用于获取用户的文字输入和图片附件。 当需要用户确认或额外指导时可调用此工具。
Args: project_directory: 项目目录路径，用于上下文定位 summary: 当前工作的摘要说明，供用户参考 timeout: 等待用户输入的超时时间（秒）
Returns: list: 用户输入内容，包含文字和可选的图片
Inputs: project_directory, summary, timeout. Returns: result
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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Scored across 2 tools
The two tools have clearly distinct purposes: get_system_info retrieves system environment information, while shouji provides an interactive web interface for collecting user input and attachments. There is no overlap in functionality, making it easy for an agent to choose the correct tool for each task.
The naming is inconsistent: get_system_info follows a clear verb_noun pattern, but shouji uses a non-English term (Chinese for 'collect') without a descriptive verb, breaking the pattern. This mix of conventions could confuse agents expecting a uniform naming style.
With only 2 tools, the server feels thin for a general-purpose feedback system, as it lacks tools for processing, analyzing, or managing feedback beyond collection. However, the count is borderline appropriate if the scope is limited to basic input gathering and system info retrieval.
For a feedback-enhanced server, there are significant gaps: it provides input collection and system info but lacks tools for storing, retrieving, analyzing, or responding to feedback. This incomplete surface will likely cause agent failures when trying to handle feedback workflows beyond simple input capture.
Feedback, roadmap, agent task execution with review, changelog, status pages and error logs.
Visual feedback from your website's visitors as tasks for your coding agent.
Human feedback for AI agents: share HTML, get a live review link, read anchored notes as markdown.
Comment on AI-generated webpages; feedback flows back to your coding agent. Free, MIT, local-first.
