semvec/pypi-packages huggingface dataset:
iflow-mcp_mcp-pyautogui-server 0.1.2
MCP server for pyautogui
License: MIT
Requires Python: >=3.10
Keywords: automation, gui, mcp, pyautogui

# mcp-pyautogui-server

[![smithery badge](https://smithery.ai/badge/@hetaoBackend/mcp-pyautogui-server)](https://smithery.ai/server/@hetaoBackend/mcp-pyautogui-server)

A MCP (Model Context Protocol) server that provides automated GUI testing and control capabilities through PyAutoGUI.

## Features

* Control mouse movements and clicks
* Simulate keyboard input
* Take screenshots
* Find images on screen
* Get screen information
* Cross-platform support (Windows, macOS, Linux)

## Tools

The server implements the following tools:

### Mouse Control
* Move mouse to specific coordinates
* Click at current or specified position
* Drag and drop operations
* Get current mouse position

### Keyboard Control  
* Type text
* Press individual keys
* Hotkey combinations

### Screen Operations
* Take screenshots
* Get screen size
* Find image locations on screen
* Get pixel colors

## Installation

### Prerequisites

* Python 3.12+
* PyAutoGUI
* Other dependencies will be installed automatically

### Install Steps

Install the package:

```bash
pip install mcp-pyautogui-server
```

### Claude Desktop Configuration

On MacOS:
```bash
~/Library/Application\ Support/Claude/claude_desktop_config.json
```

On Windows:
```bash
%APPDATA%/Claude/claude_desktop_config.json
```

Development/Unpublished Servers Configuration:
```json
{
  "mcpServers": {
    "mcp-pyautogui-server": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/mcp-pyautogui-server",
        "run",
        "mcp-pyautogui-server"
      ]
    }
  }
}
```

Published Servers Configuration:
```json
{
  "mcpServers": {
    "mcp-pyautogui-server": {
      "command": "uvx",
      "args": [
        "mcp-pyautogui-server"
      ]
    }
  }
}
```

## Development

### Building and Publishing

1. Sync dependencies and update lockfile:
```bash
uv sync
```

2. Build package distributions:
```bash
uv build
```

3. Publish to PyPI:
```bash
uv publish
```

Note: Set PyPI credentials via environment variables or command flags:
* Token: `--token` or `UV_PUBLISH_TOKEN`
* Username/password: `--username`/`UV_PUBLISH_USERNAME` and `--password`/`UV_PUBLISH_PASSWORD`

### Debugging

For the best debugging experience, use the MCP Inspector.

Launch the MCP Inspector via npm:

```bash
npx @modelcontextprotocol/inspector uv --directory /path/to/mcp-pyautogui-server run mcp-pyautogui-server
```

The Inspector will display a URL that you can access in your browser to begin debugging.

## License

This project is licensed under the MIT License - see the LICENSE file for details.
iflow-mcp_aws-pricing-mcp 1.0.0
A Model Context Protocol (MCP) server that provides AWS EC2 instance pricing data
License: MIT

# AWS Pricing MCP

A Model Context Protocol (MCP) server that provides AWS EC2 instance pricing data. This project includes both a traditional server implementation and a serverless Lambda function.

## Quick Start

### Lambda Deployment (Recommended)

The Lambda function provides the same functionality as the server but with serverless benefits:

```bash
# Build and deploy
sam build
sam deploy --guided

# Get the Function URL
aws cloudformation describe-stacks \
  --stack-name aws-pricing-mcp \
  --query 'Stacks[0].Outputs[?OutputKey==`FunctionUrl`].OutputValue' \
  --output text
```

For detailed Lambda documentation, see [LAMBDA.md](LAMBDA.md).

### Server Deployment

```bash
# Install dependencies
pip install -r requirements.txt

# Run the server
python src/server.py
```

## Features

- **EC2 Pricing Data**: Find the cheapest EC2 instances based on specifications
- **Multiple Pricing Models**: On Demand, Reserved Instances, CloudFix RightSpend
- **Flexible Filtering**: Region, platform, tenancy, vCPU, RAM, GPU, etc.
- **JSON-RPC 2.0**: Full MCP protocol compliance
- **Serverless Option**: Lambda function with Function URL
- **Dynamic Data**: Always up-to-date pricing from S3

## Documentation

- [LAMBDA.md](LAMBDA.md) - Comprehensive Lambda documentation
- [MCP.md](MCP.md) - MCP protocol examples
- [PRICING.md](PRICING.md) - Pricing data format and sources
- [BUILD.md](BUILD.md) - Build instructions

## License

[LICENSE](LICENSE)
iflow-mcp_claude-code-gemini-mcp 1.0.0
Claude Code + Gemini MCP Server - Connect Claude Code with Google's Gemini AI for powerful AI collaboration
License: MIT
Requires Python: >=3.8

# Claude Code + Gemini MCP Server

Connect Claude Code with Google's Gemini AI for powerful AI collaboration. Ask Gemini questions, get code reviews, and brainstorm ideas - all within Claude Code!

## 🚀 Quick Start (2 minutes)

### Prerequisites
- Python 3.8+ installed
- Claude Code CLI installed
- Google Gemini API key ([Get one free](https://aistudio.google.com/apikey))

### One-Line Install

```bash
curl -sSL https://raw.githubusercontent.com/RaiAnsar/claude_code-gemini-mcp/main/install.sh | bash
```

### Manual Install

1. **Clone this repo:**
```bash
git clone https://github.com/RaiAnsar/claude_code-gemini-mcp.git
cd claude_code-gemini-mcp
```

2. **Run setup with your API key:**
```bash
./setup.sh YOUR_GEMINI_API_KEY
```

That's it! 🎉

## 📖 Usage

Start Claude Code anywhere and use these commands:

```bash
claude

# Ask Gemini anything

mcp__gemini-collab__ask_gemini
  prompt: "Explain quantum computing in simple terms"

# Get code reviews
mcp__gemini-collab__gemini_code_review
  code: "def auth(u): return u.pwd == 'admin'"
  focus: "security"

# Brainstorm ideas
mcp__gemini-collab__gemini_brainstorm
  topic: "How to scale a web app to 1M users"

Or simply ask claude code to correlate with Gemini, it is not a rocket sciene... (Author's note) 
```

## 🛠️ What This Does

1. Installs the Google Gemini Python SDK
2. Sets up an MCP server that bridges Claude Code and Gemini
3. Configures it globally (works in any directory)
4. Provides tools for collaboration between Claude and Gemini

## 🔧 Available Tools

- **ask_gemini** - Ask Gemini any question
- **gemini_code_review** - Get security/performance code reviews
- **gemini_brainstorm** - Brainstorm ideas and solutions

## 📁 Installation Location

The server is installed at: `~/.claude-mcp-servers/gemini-collab/`

## 🐛 Troubleshooting

**MCP not showing up?**
```bash
# Check if it's installed
claude mcp list

# Reinstall with global scope
claude mcp remove gemini-collab
claude mcp add --scope user gemini-collab python3 ~/.claude-mcp-servers/gemini-collab/server.py
```

**Connection errors?**
- Check your API key is valid
- Ensure Python has `google-generativeai` installed: `pip install google-generativeai`

## 🔑 Update API Key

Edit `~/.claude-mcp-servers/gemini-collab/server.py` and replace the API key.

## 🤝 Contributing

Pull requests welcome! Please keep it simple and beginner-friendly.

## 📜 License

MIT - Use freely!

---

Made with ❤️ for the Claude Code community
XGM-devtest-qa 1.1.2
QA设备驱动 - XGM MDS24 设备驱动包
License: MIT
Requires Python: >=3.7
Keywords: device, driver, qa, xgm, mds24

# SystemQ - QA设备驱动

QA设备驱动包，用于控制 XGM MDS24 设备。

## 安装

```bash
pip install systemq
```

## 使用方法

### 1. 启动服务

```bash
quark remote remote.json
```

### 2. 控制设备

```python
from quark import connect

adc = connect('ADC', host='127.0.0.1', port=1169)
```

## 依赖

- quarkstudio[remote]
- waveforms
- numpy

## 版本信息

- QA驱动版本: V1.0-20250523
- QA-SQ版本: V1.2-20250719

## 许可证

MIT License