semvec/pypi-packages huggingface dataset:
vkey-calculator-mcp 0.1.4
vkey mcp calculator
Requires Python: >=3.13

# VKey Calculator MCP

一个基于 MCP (Model Context Protocol) 的计算器服务器，提供数学计算功能。

## 功能特性

- **基础算术运算**：加法、减法、乘法、除法
- **高级数学运算**：幂运算、平方根、阶乘
- **错误处理**：除零检查等安全机制
- **MCP 协议**：支持与 MCP 客户端集成

## 安装

### 方法一：使用 uv（推荐）

```bash
# 克隆项目
git clone <repository-url>
cd vkey-calculator-mcp

# 安装依赖并运行
uv run -m vkey_calculator_kel
```

### 方法二：本地安装

```bash
# 克隆项目
git clone <repository-url>
cd vkey-calculator-mcp

# 安装项目
pip install -e .

# 运行
python -m vkey_calculator_kel
```

## MCP 配置

在你的 MCP 客户端配置文件中添加以下配置：

```json
{
  "mcpServers": {
    "vkey_calculator_kel": {
      "command": "uvx",
      "args": [
        "vkey_calculator_kel@latest"
      ]
    }
  }
}
```

## 可用的计算工具

### 基础算术
- `add(a, b)` - 加法运算
- `subtract(a, b)` - 减法运算  
- `multiply(a, b)` - 乘法运算
- `divide(a, b)` - 除法运算（自动检查除零）

### 高级数学
- `power(base, exponent)` - 幂运算
- `sqrt(number)` - 平方根计算
- `factorial(n)` - 阶乘计算

## 开发

### 项目结构
```
vkey-calculator-mcp/
├── src/
│   └── vkey_calculator_kel/
│       ├── __init__.py      # 主入口
│       ├── __main__.py      # 模块入口
│       └── server.py        # MCP 服务器实现
├── pyproject.toml           # 项目配置
└── README.md               # 项目说明
```

### 构建
```bash
# 构建包
uv run -m build

# 安装开发版本
pip install -e .
```

## 许可证

MIT License
Technicmovehub 0.1.1
A Python library for controlling the LEGO Technic Move Hub via Bluetooth Low Energy (BLE).
Requires Python: >=3.10

# Technic Move Hub Python Library

A Python library for controlling the LEGO Technic Move Hub via Bluetooth Low Energy (BLE). This library aims to provide a simple, synchronous interface for controlling your LEGO Move Hub.

## Features

* Connect and disconnect from the LEGO Technic Move Hub.
* Set the LED color.
* Control motors (A, B, C) with speed and direction (forward/backward).
* Handles asynchronous BLE communication internally, providing a synchronous API for the user.
* Graceful program exit if the hub is not connected when a command is issued.

## Installation

You can install this library directly from PyPI using pip:

```bash
pip install technicmovehub
quantiq 0.2.0
Confidence-aware AI toolkit with uncertainty estimation for Transformers and Deep Ensembles
Requires Python: >=3.7

# QuantIQ 🔍

**QuantIQ** is a unified Python library for building robust, uncertainty-aware deep learning systems. It brings together lightweight, modular tools to help researchers and practitioners gain insight into model reliability and risk through principled uncertainty estimation.
<img width="1024" height="939" alt="poster" src="https://github.com/user-attachments/assets/12d53dab-8cd9-4c2b-b5b8-7bf203007ff5" />


## 📦 What's Inside

Quantiq currently includes two powerful uncertainty quantification tools:

### 1. DropWise 🔁
A plug-and-play PyTorch/HuggingFace wrapper for Monte Carlo Dropout–based uncertainty estimation in Transformers.

- Supports classification, regression, QA, and token tagging
- Computes entropy, confidence, and class-wise variances
- Enables dropout during inference for Bayesian-style sampling

📖 [Full DropWise Documentation](https://github.com/aryanator/QuantIQ/blob/main/quantiq/dropwise/README.md)

---

### 2. SmartEnsemble 🧠
A deep ensemble wrapper for PyTorch models with support for adversarial training and dual-mode (epistemic + aleatoric) uncertainty estimation.

- Works with any PyTorch model
- Enables risk scoring and calibration
- Includes built-in visualization and prediction APIs

📖 [Full SmartEnsemble Documentation](https://github.com/aryanator/QuantIQ/blob/main/quantiq/smartensemble/README.md)

---

## 🔧 Installation

```bash
pip install quantiq
```

Or install from source:

```bash
git clone https://github.com/aryanator/QuantIQ.git
cd quantiq
pip install -e .
```

---

## 🧪 Use Cases

- Safety-critical predictions (medical AI, self-driving, finance)
- Uncertainty-aware active learning
- Robust ML pipelines with explainable confidence
- Research experiments involving confidence, entropy, risk

---

## 📚 Documentation & Examples

Explore examples, API usage, and task-specific walkthroughs in the GitHub repository:  
🔗 https://github.com/aryanator/QuantIQ


<img width="1189" height="590" alt="image" src="https://github.com/user-attachments/assets/16fcbc24-5f7e-463e-91af-f7b1806b90c5" />


---

## 📝 License

MIT License

---

Built by [Aryan Patil](https://github.com/aryanator) to make uncertainty estimation simpler, smarter, and production-ready.