semvec/pypi-packages huggingface dataset:
iflow-mcp_user-feedback-mcp 0.1.0
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Requires Python: >=3.11

# User Feedback MCP

Simple [MCP Server](https://modelcontextprotocol.io/introduction) to enable a human-in-the-loop workflow in tools like [Cline](https://cline.bot) and [Cursor](https://www.cursor.com). This is especially useful for developing desktop applications that require complex user interactions to test.

![Screenshot showing the feedback UI](https://github.com/mrexodia/user-feedback-mcp/blob/main/.github/feedback-ui.png?raw=true)

## 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:

```json
{
  "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](https://taskfile.dev).

## Installation (Cline)

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

![Screenshot showing installation steps](https://github.com/mrexodia/user-feedback-mcp/blob/main/.github/cline-installation.png?raw=true)

1. Install [uv](https://github.com/astral-sh/uv) globally:
   - Windows: `pip install uv`
   - Linux/Mac: `curl -LsSf https://astral.sh/uv/install.sh | sh`
2. Clone this repository, for this example `C:\MCP\user-feedback-mcp`.
3. Navigate to the Cline _MCP Servers_ configuration (see screenshot).
4. Click on the _Installed_ tab.
5. Click on _Configure MCP Servers_, which will open `cline_mcp_settings.json`.
6. Add the `user-feedback-mcp` server:

```json
{
  "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

```sh
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>
```
quantumvitas 0.0.1
Placeholder package for QuantumVITAS (legacy GUI project).
Requires Python: >=3.8

# QuantumVITAS (Legacy Placeholder)

This is a placeholder package to reserve the `quantumvitas` name on PyPI.  
The original QuantumVITAS GUI project has been superseded by **QMatSuite**.

This package contains no functional code.
GearAPI 0.15.11
An API wrapper for cumulocity API. Made for researcher. Focus on Measurements and events resources from GEAR data
Requires Python: >=3.6

This is a Python client for the Cumulocity Data API. The GearAPI package is a wrapper to simplify GET requests and JSON response parsing from the Measurement and Event Resources. 

This library abstracts:
1. API endpoint handling
2. pagnation handling
3. API retry
4. file handling
5. Handling API from multiple devices 

## how to use

2. `pip install GearAPI`


```
from GearAPI import Client

client = Client(
    user = <IoT Platform User Name>
    password = <IoT Platform User Password>
)

date_start = "2025-03-01"
date_end = "2025-03-02"
device_params = {
    "devicetype": "iaq"
}
output_file_path = "output.csv"

client.download(
    date_start, 
    date_end, 
    output_file_path = output_file_path, 
    device_params = device_params)

"""
output: all the iaq devices data from 2024-08-01 to 2024-08-02
"""
```

go to https://github.com/kajimadev-KaTRIS/GearAPI/deployments/github-pages and click on the latest weblink for more information


## data in csv
some of the data doesn't open properly in excel .csv (e.g. oura ring heart rate data) due to limited size. 
please use pandas to visualise the data for now.