semvec/pypi-packages huggingface dataset:
openg2p-g2p-bridge-example-bank-api 1.1.0
OpenG2P G2P Bridge API
Requires Python: >=3.7

#  openg2p-g2p-bridge-api

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[![openapi](https://img.shields.io/badge/open--API-swagger-brightgreen)](https://validator.swagger.io/?url=https://raw.githubusercontent.com/OpenG2P/openg2p-g2p-bridge-api/develop/api-docs/generated/openapi.json)
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## Licenses

This repository is licensed under [MPL-2.0](LICENSE).
weatherstack-mcp-server 0.1.1
An MCP server for interacting with the Weatherstack API via Model Context Protocol tools, providing access to current, historical, forecast, and marine weather data.
Requires Python: >=3.13
Keywords: ai, automation, forecast, llm, marine data, mcp, weather, weather history, weatherstack

# ⛅ Weatherstack MCP Server

This is an **MCP (Model Context Protocol) server** for integrating with the [Weatherstack API](https://weatherstack.com/), enabling AI agents to access real-time, historical, forecast, and marine weather data using natural language input.

The goal of this project is to expose Weatherstack's functionality through MCP-compatible tools that can be used seamlessly by large language models and agent frameworks.

---

## 🧠 What is MCP?

MCP (Model Context Protocol) is a lightweight protocol designed to let **AI agents interact with external tools and APIs** in a structured and modular way. Think of it like **USB for AI** — this server acts as a "driver" for the Weatherstack platform.

With this MCP server, AI models can:

- 🌤️ Get current weather conditions for any location  
- 🕰️ Access historical weather data by date and hour  
- 📅 Retrieve multi-day weather forecasts  
- 🌊 Get marine/sailing weather and tide information  

---

# 🚀 How to Run

To use this MCP server, you'll need:

## ✅ Prerequisites

- Python 3.11+  
- [`uv`](https://github.com/astral-sh/uv) – a modern Python package manager  
- A supported LLM (e.g., Claude)  
- A Weatherstack API key – get it at [weatherstack.com](https://weatherstack.com/)

## Add this to Claude Desktop config

```json
{
  "mcpServers": {
    "weatherstack-mcp": {
      "command": "uvx",
      "args": [
        "weatherstack-mcp-server",
        "--api-key",
        "YOUR WEATHERSTACK API KEY"
      ]
    }
  }
}
```

---

## 🤝 Contributions Welcome!

Whether you're passionate about weather tech, AI agent development, or robust API integrations — we’d love your help improving this project. You can contribute by:

- Adding support for additional Weatherstack endpoints  
- Improving the structure of tool responses  
- Writing better tests and usage examples  
- Sharing feedback or ideas via Issues or Discussions

Feel free to fork, explore, and open a PR. Let’s empower agents with better environmental awareness — one forecast at a time. 🌍🛰️

---

**MCP-FORGE** – Building tools for the future of intelligent automation.
voai-client 0.2.0
Python SDK for voai.ai VoiceAPI (TTS / generate-voice / generate-dialogue)
Requires Python: >=3.8

# voai‑client — Python SDK & CLI for **voai.ai VoiceAPI**

`voai‑client` 旨在讓您用最簡單的方式存取 **voai.ai** 的雲端文字轉語音服務，完整封裝以下 API：

| 功能 | VoiceAPI 方法 | HTTP 端點 |
|------|---------------|-----------|
| 取得 Speaker 列表 | `get_speakers()` | `GET /TTS/GetSpeaker` |
| 短文本 TTS | `speech()` | `POST /TTS/Speech` |
| 長文本 TTS | `generate_voice()` | `POST /TTS/generate-voice` |
| 多角色對話 TTS | `generate_dialogue()` | `POST /TTS/generate-dialogue` |
| 查詢金鑰用量 | `get_usage()` | `GET /Key/Usage` |

---
## 安裝


### 從程式庫安裝
```
pip install -e .
```

### 從 pypi 安裝
```bash
pip install voai-client
```

---
## 快速上手
### 建立 `VoiceAPI` 實例
```python
from voai_client import VoiceAPI
api = VoiceAPI("YOUR_API_KEY")  # x-api-key 會自動附加到所有請求
```

### 1️⃣ 取得 Speaker 列表
```python
speakers = api.get_speakers()
print(speakers[0])  # ➜ {"name": "佑希", "language": "zh-TW", ...}
```

### 2️⃣ 短文本 TTS
```python
wav = api.speech(
    text="網際智慧的聲音，是業界的標竿。",
    speaker="佑希",
    version="Neo",
    style="預設",
)
api.save_audio(wav, "short.wav")
```

### 3️⃣ 長文本 TTS（支援 `[:秒]` 停頓標籤）
```python
long_text = (
    "網際智慧的聲音，不僅清晰自然，還融入了情感與真實感，"
    "展現了[:2]先進技術的極致魅力。它是業界的標竿。"
)
wav = api.generate_voice(long_text, name="佑希")
api.save_audio(wav, "long.wav")
```

### 4️⃣ 多角色對話 TTS
```python
dialogue = [
    {"voai_script_text": "今天的進度會議[:1.5]你準備好了嗎？", "preset_id": "neo_佑希"},
    {"voai_script_text": "差不多了，我剛把簡報做完。", "preset_id": "default_子墨"},
]

presets = [
    {"id": "neo_佑希", "voice": {"name": "佑希", "style": "聊天", "version": "Neo"}},
    {"id": "default_子墨", "voice": {"name": "子墨", "style": "預設", "version": "Classic"}},
]

wav = api.generate_dialogue(dialogue, preset_speakers=presets)
api.save_audio(wav, "dialogue.wav")
```

### 5️⃣ 查詢 API 用量
```python
usage = api.get_usage()
print(usage)  # ➜ {'quota': 100000, 'remaining': 98234, ...}
```

---
## CLI 用法
安裝後會自帶 `voai-cli`，所有子指令皆需 `--api-key`：

```bash
# 列出所有 speaker
voai-cli --api-key $VOAI_KEY speakers

# 短文本 TTS
voai-cli --api-key $VOAI_KEY speech "你好世界" 佑希 --outfile hello.wav

# 長文本 TTS（讀取文字檔）
voai-cli --api-key $VOAI_KEY generate article.txt 佑希 --outfile article.wav

# 多角色對話 TTS（讀取 JSON）
voai-cli --api-key $VOAI_KEY dialogue dialogue.json --outfile talk.wav

# 查詢金鑰用量
voai-cli --api-key $VOAI_KEY usage
```

`dialogue.json` 範例：
```json
{
  "input": {
    "preset_speakers": [
      {"id": "neo_佑希", "voice": {"name": "佑希", "style": "聊天", "version": "Neo"}},
      {"id": "default_子墨", "voice": {"name": "子墨", "style": "預設", "version": "Classic"}}
    ],
    "dialogue": [
      {"voai_script_text": "今天的進度會議[:1.5]你準備好了嗎？", "preset_id": "neo_佑希"},
      {"voai_script_text": "差不多了，我剛把簡報做完。", "preset_id": "default_子墨"}
    ]
  }
}
```

---
## 參數對照表
| 參數 | 說明 | 範圍 / 預設 |
|-------|------|-------------|
| `speed` | 語速 | `0.5 – 1.5`，預設 `1` |
| `pitch_shift` | 音調 | `‑5 – 5`，預設 `0` |
| `style_weight` | 風格權重（僅 Classic 有效）| `0 – 1`，預設 `0` |
| `breath_pause` | 句間停頓（秒）| `0 – 10`，預設 `0` |

> **停頓標籤**：`[:秒]` 支援小數點後一位，最大 `5` 秒；任兩標籤間建議至少 20 字以保持自然度。
chat-content 0.1.1
Fetch responses from GCP Discovery Engine (Agent Builder Chat App)
Requires Python: >=3.7

# chat-content

Fetch full search responses from GCP Discovery Engine (Agent Builder Chat App).

## Features

- Queries Discovery Engine using GCP's `google-cloud-discoveryengine` API
- Supports snippet and summary fetching
- Clean function-based interface

## Installation

```bash
pip install chat-content
calc-facil 1.0.0
Uma calculadora simples para operações básicas
Requires Python: >=3.6

# calc-facil

Uma calculadora simples com operações básicas (soma, subtração, multiplicação, divisão).

Este projeto foi criado como parte de um treinamento sobre como criar e publicar pacotes Python no PyPI.

## Instalação

```bash
pip install calc-facil
```

## Uso

```python
from calc_facil import Calculadora

# Criar uma instância da calculadora
calc = Calculadora()

# Realizar operações
soma = calc.soma(5, 3) # 8
diferenca = calc.subtracao(5, 3) # 2
produto = calc.multiplicacao(5, 3) # 15
quociente = calc.divisao(6, 3) # 2.0

# Tratamento de erros
try:
    resultado = calc.divisao(5, 0)
except ZeroDivisionError as e:
    print(f'Erro: {e}')
```

## Uso via linha de comando (CLI)

Após a instalação, é possível utilizar o comando calc-facil diretamente no terminal:

```bash
calc-facil <operacao> <a> <b>
```

Exemplos:

```bash
calc-facil soma 2 3
# Resultado: 5.0

calc-facil divisao 10 2
# Resultado: 5.0
```

## As operações disponíveis são:

- soma
- subtracao
- multiplicacao
- divisao

## Operações disponíveis

- **soma(a, b)**: Retorna a soma de a e b
- **subtracao(a, b)**: Retorna a diferença entre a e b
- **multiplicacao(a, b)**: Retorna o produto de a e b
- **divisao(a, b)**: Retorna o quociente da divisão de a por b (lança ZeroDivisionError se b for zero)

## Pré-requisitos

- Python 3.6 ou superior

## Licença

Este projeto está licenciado sob a Licença **MIT**. Veja o arquivo [LICENSE](./LICENSE) para mais detalhes.
llama-index-readers-oxylabs 0.1.2
llama-index readers oxylabs integration
Requires Python: <4.0,>=3.9

# LlamaIndex Oxylabs Reader Integration

Use Oxylabs Reader to load the data from search engines, e-commerce sites, travel platforms, and any other website.
For more information checkout out the [Oxylabs documentation](https://developers.oxylabs.io/scraper-apis/web-scraper-api).

## Instructions for OxylabsReader

### Setup and Installation

Installation with `pip`

```shell
pip install llama-index-readers-oxylabs
```

Installation with `poetry`

```shell
poetry add llama-index-readers-oxylabs
```

Installation with `uv`

```shell
uv add llama-index-readers-oxylabs
```

### Get Oxylabs credentials

[Set up](https://oxylabs.io/) your Oxylabs account and get the username and password.

### Using OxylabsReader

```python
from llama_index.readers.oxylabs import OxylabsGoogleSearchReader


reader = OxylabsGoogleSearchReader(
    username="OXYLABS_USERNAME",
    password="OXYLABS_PASSWORD",
)

docs = reader.load_data(
    {"query": "Iphone 16", "parse": True, "geo_location": "Berlin, Germany"}
)

print(docs[0].text)
```