Simple MCP Server to enable a human-in-the-loop workflow in tools like Cline and Cursor.
- • Basic MCP protocol features implemented (16/40)
- • Room for improvement in GitHub community
- • Optimal dependency management (20/20)
- • Room for improvement in deployment maturity
- • Documentation (8/8)
- • Archestra MCP Trust score badge is missing
{
"github.com/mrexodia/user-feedback-mcp": {
"command": "uv",
"args": [
"--directory",
"c:\\MCP\\user-feedback-mcp",
"run",
"server.py"
],
"env": {}
},
"github.com/mrexodia/user-feedback-mcp-npm-dev": {
"command": "npm",
"args": [
"run",
"dev"
],
"env": {}
},
"github.com/mrexodia/user-feedback-mcp-fastmcp-dev": {
"command": "uv",
"args": [
"run",
"fastmcp",
"dev",
"server.py"
],
"env": {}
}
}User Feedback MCP
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.

Prompt Engineering
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.
Installation (Cline)
To install the MCP server in Cline, follow these steps (see screenshot):

- Install uv globally:
- Windows:
pip install uv - Linux/Mac:
curl -LsSf https://astral.sh/uv/install.sh | sh
- Windows:
- Clone this repository, for this example
C:\MCP\user-feedback-mcp. - Navigate to the Cline MCP Servers configuration (see screenshot).
- Click on the Installed tab.
- Click on Configure MCP Servers, which will open
cline_mcp_settings.json. - Add the
user-feedback-mcpserver:
{
"mcpServers": {
"github.com/mrexodia/user-feedback-mcp": {
"command": "uv",
"args": [
"--directory",
"c:\\MCP\\user-feedback-mcp",
"run",
"server.py"
],
"timeout": 600,
"autoApprove": [
"user_feedback"
]
}
}
}
Development
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.
Available tools
<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>
[](https://archestra.ai/mcp-catalog/mrexodia__user-feedback-mcp)User Feedback MCP
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.

Prompt Engineering
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.
Installation (Cline)
To install the MCP server in Cline, follow these steps (see screenshot):

- Install uv globally:
- Windows:
pip install uv - Linux/Mac:
curl -LsSf https://astral.sh/uv/install.sh | sh
- Windows:
- Clone this repository, for this example
C:\MCP\user-feedback-mcp. - Navigate to the Cline MCP Servers configuration (see screenshot).
- Click on the Installed tab.
- Click on Configure MCP Servers, which will open
cline_mcp_settings.json. - Add the
user-feedback-mcpserver:
{
"mcpServers": {
"github.com/mrexodia/user-feedback-mcp": {
"command": "uv",
"args": [
"--directory",
"c:\\MCP\\user-feedback-mcp",
"run",
"server.py"
],
"timeout": 600,
"autoApprove": [
"user_feedback"
]
}
}
}
Development
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.
Available tools
<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>
Related MCP Servers
Agent-MCP
89/100Agent-MCP is a framework for creating multi-agent systems that enables coordinated, efficient AI collaboration through the Model Context Protocol (MCP). The system is designed for developers building AI applications that benefit from multiple specialized agents working in parallel on different aspects of a project.
serena
85/100A powerful coding agent toolkit providing semantic retrieval and editing capabilities (MCP server & other integrations)
JFrog MCP Server
85/100Official JFrog MCP server that enables AI assistants to interact with the JFrog Platform. Supports repository management, build tracking, runtime monitoring, artifact searching, package intelligence, and Xray security scanning.
context7
82/100Context7 MCP Server -- Up-to-date code documentation for LLMs and AI code editors
gk-cli
81/100GitKraken CLI Releases and Documentation
Excalidraw
80/100Remote MCP server for Excalidraw - streams hand-drawn diagrams with smooth viewport camera control and interactive fullscreen editing
