@Minidoracat
基本情報
概要はまだありません
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"mcp-feedback-enhanced": {
"command": "uvx",
"args": [
"mcp-feedback-enhanced@latest"
],
"timeout": 600,
"autoApprove": [
"interactive_feedback"
]
}
}
}Interactive feedback collection tool for LLM agents. USAGE RULES: 1. During any process, task, or conversation, whether asking, replying, or completing phased tasks, you must call this tool to ask for feedback. 2. Unless receiving termination instructions, all steps must repeatedly call this tool. 3. Whenever user feedback is received, if the feedback content is not empty, you must call this tool again and adjust behavior based on the feedback content. 4. Only when the user explicitly indicates "end" or "no more interaction needed" can you stop calling this tool, and the process is considered complete. 5. You should summarize what have done, and provide project directory through args to let user know what you have done to provide feedback for next step. Args: project_directory: Project directory path for context summary: Summary of AI work completed for user review timeout: Timeout in seconds for waiting user feedback (default: 600 seconds) Returns: list: List containing TextContent and MCPImage objects representing user feedback
獲取系統環境資訊 Returns: str: JSON 格式的系統資訊
An MCP server that establishes feedback-oriented development workflows with Web UI and Desktop Application dual interfaces. It guides AI to confirm with users before taking actions, consolidating multiple tool calls into a single feedback request. Designed for local, SSH remote, and WSL environments; compatible with Cursor, Cline, Windsurf, Augment, and Trae.
Install Python package uv then configure your MCP client with JSON using the command uvx mcp-feedback-enhanced@latest. Set environment variables like MCP_DESKTOP_MODE or MCP_WEB_HOST as needed. Add the prompt “follow mcp-feedback-enhanced instructions” to your AI assistant for best results. Test with uvx mcp-feedback-enhanced@latest test --web or --desktop.
Both provide exactly the same functionality. The Desktop Application is a cross-platform native app based on Tauri; the Web UI runs in a browser and is ideal for remote and WSL environments.
Python and `uv` must be installed. For Web UI mode no GUI dependencies are needed. Desktop mode additionally requires the Tauri framework (build tools) but the README does not list explicit Node.js or system dependencies.
Session data is stored locally in the file system. The README mentions “local file storage” and supports export to JSON, CSV, and Markdown.
Yes. It supports PNG, JPG, JPEG, GIF, BMP, and WebP formats, drag-and-drop, clipboard paste (Ctrl+V), and any image size with automatic intelligent processing.
Set the environment variable `MCP_DESKTOP_MODE=true` in your MCP configuration. The interface will then launch the native desktop application instead of the web browser.
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