Simple MCP Server to enable a human-in-the-loop workflow in tools like Cline and Cursor.
Simple MCP Server to enable a human-in-the-loop workflow in tools like Cline and Cursor. This is especially useful for developing desktop applications that require complex user interactions to test.
For the best results, add the following to your custom prompt:
Before completing the task, use the user_feedback MCP tool to ask the user for feedback.
This will ensure Cline uses this MCP server to request user feedback before marking the task as completed.
.user-feedback.json
Hitting Save Configuration creates a .user-feedback.json
file in your project directory that looks like this:
{
"command": "npm run dev",
"execute_automatically": false
}
This configuration will be loaded on startup and if execute_automatically
is enabled your command
will be instantly executed (you will not have to click Run manually). For multi-step commands you should use something like Task.
To install the MCP server in Cline, follow these steps (see screenshot):
pip install uv
curl -LsSf https://astral.sh/uv/install.sh | sh
C:\MCP\user-feedback-mcp
.cline_mcp_settings.json
.user-feedback-mcp
server:{
"mcpServers": {
"github.com/mrexodia/user-feedback-mcp": {
"command": "uv",
"args": [
"--directory",
"c:\\MCP\\user-feedback-mcp",
"run",
"server.py"
],
"timeout": 600,
"autoApprove": [
"user_feedback"
]
}
}
}
uv run fastmcp dev server.py
This will open a web interface at http://localhost:5173 and allow you to interact with the MCP tools for testing.
<use_mcp_tool>
<server_name>github.com/mrexodia/user-feedback-mcp</server_name>
<tool_name>user_feedback</tool_name>
<arguments>
{
"project_directory": "C:/MCP/user-feedback-mcp",
"summary": "I've implemented the changes you requested."
}
</arguments>
</use_mcp_tool>
Interact with Wizzypedia through the MediaWiki API, supporting both read-only and authenticated operations.
An MCP server for integrating with the NATS messaging system.
Enables interactive LLM workflows by adding local user prompts and chat capabilities directly into the MCP loop.
Enables AI models to prompt users for input directly within their code editor for interactive conversations.
An MCP server for collecting interactive user feedback through a graphical user interface.
Interact with Slack workspaces to read and send messages directly through your AI assistant.
An MCP server for interacting with Google Meet through the Google Calendar API.
Enables AI assistants to send push notifications via the Pushover service.
Manage your X (Twitter) account using the Apex social media infrastructure. Requires an Apex API Key.
Interact with Mailgun API.