semvec/pypi-packages huggingface dataset:
memealerts 1.1.4
Memealerts async/sync client
Requires Python: >=3.11
Keywords: meme, memealert, memealerts, memes, twitch

# Memealerts Python Client

This project is a library which provides you clients for interaction with memealerts.

# Install

`pip install memealerts`

# Usage

## Using sync client
```python
from memealerts import MemealertsClient

token = "<Your bearer token>"

cli = MemealertsClient(token)

supporters = cli.get_supporters()
first_supporter = supporters.data[0].supporterId
cli.give_bonus(first_supporter, 5)
```

## Using async client

```python
import asyncio
from memealerts import MemealertsAsyncClient

token = "<Your bearer token>"

cli = MemealertsAsyncClient(token)

async def main():
    supporters = await cli.get_supporters()
    first_supporter = supporters.data[0].supporterId
    await cli.give_bonus(first_supporter, 5)

asyncio.run(main())
```

# Badges

[![wakatime](https://wakatime.com/badge/github/Quantum-0/memealerts.svg)](https://wakatime.com/badge/github/Quantum-0/memealerts)
[![Black](https://github.com/Quantum-0/memealerts/actions/workflows/black.yml/badge.svg)](https://github.com/Quantum-0/memealerts/actions/workflows/black.yml)
[![GitHub Org's stars](https://img.shields.io/github/stars/quantum-0/memealerts)](https://github.com/Quantum-0/memealerts/)
![PyPI - License](https://img.shields.io/pypi/l/memealerts)
[![PyPI](https://img.shields.io/pypi/v/memealerts)](https://pypi.org/project/memealerts/)
![PyPI - Status](https://img.shields.io/pypi/status/memealerts)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/memealerts)
![PyPI - Downloads](https://img.shields.io/pypi/dm/memealerts)


# License

[MIT License](./LICENSE)

# TODO

- handle errors from api
- iterator for all supporters
- user/find
- other methods and schemas
-
iflow-mcp_czh2774-cocosmcp 1.0.3
Cocos Creator MCP Log Bridge
Requires Python: >=3.7

# Cocos MCP Log Bridge

一个强大的日志桥接工具，用于在 Cocos Creator 编辑器和 Cursor AI 之间同步日志信息，帮助开发者更有效地分析和解决问题。

[![GitHub stars](https://img.shields.io/github/stars/czh2774/cocosMCP.svg)](https://github.com/czh2774/cocosMCP/stargazers)
[![License](https://img.shields.io/github/license/czh2774/cocosMCP.svg)](https://github.com/czh2774/cocosMCP/blob/main/LICENSE)

![Cocos Creator](https://img.shields.io/badge/Cocos%20Creator-3.8.0%2B-blue)
![Cursor AI](https://img.shields.io/badge/Cursor%20AI-Compatible-green)

## 🌟 功能特点

- **实时日志同步**: 直接从 Cocos Creator 编辑器获取最新日志
- **智能过滤**: 支持按类型过滤（普通日志、警告、错误）
- **关键词搜索**: 精确定位特定问题
- **一键清除**: 随时清空日志以减少干扰
- **场景信息**: 获取当前场景的基本信息和节点列表
- **场景操作**: 支持打开场景等基础操作
- **TCP 通信桥接**: 稳定可靠的通信机制
- **Cursor AI 集成**: 完全兼容 Cursor MCP 协议

## 🚀 快速入门

### 前置条件

- Cocos Creator 3.8.0 或更高版本
- Python 3.7 或更高版本
- uv 包管理器 (推荐) 或 pip

### 安装步骤

1. **克隆仓库**
   ```bash
   git clone https://github.com/czh2774/cocosMCP.git
   ```

2. **复制到 Cocos Creator 项目**
   
   将克隆的 `cocosMCP` 目录复制到你的 Cocos Creator 项目的 `extensions` 目录下。

3. **安装 Python 依赖**
   ```bash
   cd your-project/extensions/cocosMCP/Python
   uv pip install -r requirements.txt
   ```

4. **在 Cocos Creator 中启用扩展**
   
   启动 Cocos Creator，进入 `扩展 -> 扩展管理器`，确保 `cocosMCP` 扩展已启用。

5. **配置 Cursor AI**
   
   在 Cursor AI 设置中配置 MCP 服务器，指向 Python 服务器脚本。

### 基本用法

```python
# 查询日志
logs = await mcp.query_logs({
    "show_logs": True,
    "show_warnings": True,
    "show_errors": True
})

# 清除日志
await mcp.clear_logs()

# 检查连接状态
status = await mcp.connection_status()

# 获取场景信息
scene_info = await mcp.get_scene_info()

# 列出场景中的所有节点
nodes = await mcp.list_scene_nodes()

# 打开指定UUID的场景
await mcp.open_scene("scene-uuid-here")
```

## 📚 详细文档

本项目包含三个详细的文档:

- [用户使用指南](USAGE.md): 安装、配置和使用方法
- [开发者指南](DEVELOPMENT.md): 代码结构、扩展功能和维护说明
- [问题排查](TROUBLESHOOTING.md): 常见问题和解决方案

## 🔧 技术架构

Cocos MCP 由三个主要部分组成:

1. **Cocos Creator 扩展**: TypeScript 编写的编辑器扩展
2. **TCP 通信桥**: 连接编辑器和 Python 服务器
3. **Python MCP 服务器**: 处理 Cursor AI 的请求

![架构图](https://via.placeholder.com/800x400?text=Cocos+MCP+Architecture)

## 🤝 贡献指南

欢迎贡献代码、报告问题或提出新功能建议！请查看 [开发者指南](DEVELOPMENT.md) 了解详情。

## 📄 许可证

本项目采用 MIT 许可证 - 详情请参阅 [LICENSE](LICENSE) 文件。

## 🙏 致谢

- Cocos Creator 团队提供的优秀游戏引擎
- Cursor AI 团队开发的智能编程助手
- 所有贡献者和用户的支持和反馈

---

如有问题或建议，请提交 [Issues](https://github.com/czh2774/cocosMCP/issues)。
apache-iggy 0.6.4.dev1
Apache Iggy is the persistent message streaming platform written in Rust, supporting QUIC, TCP and HTTP transport protocols, capable of processing millions of messages per second.
Requires Python: >=3.10
Keywords: streaming, messaging, pubsub, iggy, rust, performance

# apache-iggy

[![discord-badge](https://img.shields.io/discord/1144142576266530928)](https://discord.gg/C5Sux5NcRa)

Apache Iggy is the persistent message streaming platform written in Rust, supporting QUIC, TCP and HTTP transport protocols, capable of processing millions of messages per second.

## Installation

### Basic Installation

```bash
pip install apache-iggy
```

### Development Installation

For testing:

```bash
pip install -e ".[testing]"
```

For development with all tools:

```bash
pip install -e ".[dev,testing]"
```

### Supported Python Versions

- Python 3.10+

## Testing

### Quick Test

```bash
# Run tests with Docker (recommended)
docker compose -f docker-compose.test.yml up --build
```

### Local Development

```bash
# Install dependencies and build
pip install -e ".[testing]"
maturin develop

# Run tests (requires iggy-server running)
pytest tests/ -v
```

## Examples

Refer to the [examples/python/](https://github.com/apache/iggy/tree/master/examples/python) directory for usage examples.

## Contributing

See [CONTRIBUTING.md](https://github.com/apache/iggy/blob/master/foreign/python/CONTRIBUTING.md) for development setup and guidelines.

## License

Licensed under the Apache License 2.0. See [LICENSE](https://github.com/apache/iggy/blob/master/foreign/python/LICENSE) for details.
iflow-mcp_amansingh0311-mcp-qdrant-openai 0.1.0
MCP Qdrant Server with OpenAI Embeddings
Requires Python: >=3.10

[![MseeP.ai Security Assessment Badge](https://mseep.net/pr/amansingh0311-mcp-qdrant-openai-badge.png)](https://mseep.ai/app/amansingh0311-mcp-qdrant-openai)

# MCP Qdrant Server with OpenAI Embeddings

[![smithery badge](https://smithery.ai/badge/@amansingh0311/mcp-qdrant-openai)](https://smithery.ai/server/@amansingh0311/mcp-qdrant-openai)

This MCP server provides vector search capabilities using Qdrant vector database and OpenAI embeddings.

## Features

- Semantic search in Qdrant collections using OpenAI embeddings
- List available collections
- View collection information

## Prerequisites

- Python 3.10+ installed
- Qdrant instance (local or remote)
- OpenAI API key

## Installation

### Installing via Smithery

To install Qdrant Vector Search Server for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@amansingh0311/mcp-qdrant-openai):

```bash
npx -y @smithery/cli install @amansingh0311/mcp-qdrant-openai --client claude
```

### Manual Installation
1. Clone this repository:

   ```bash
   git clone https://github.com/yourusername/mcp-qdrant-openai.git
   cd mcp-qdrant-openai
   ```

2. Install dependencies:
   ```bash
   pip install -r requirements.txt
   ```

## Configuration

Set the following environment variables:

- `OPENAI_API_KEY`: Your OpenAI API key
- `QDRANT_URL`: URL to your Qdrant instance (default: "http://localhost:6333")
- `QDRANT_API_KEY`: Your Qdrant API key (if applicable)

## Usage

### Run the server directly

```bash
python mcp_qdrant_server.py
```

### Run with MCP CLI

```bash
mcp dev mcp_qdrant_server.py
```

### Installing in Claude Desktop

```bash
mcp install mcp_qdrant_server.py --name "Qdrant-OpenAI"
```

## Available Tools

### query_collection

Search a Qdrant collection using semantic search with OpenAI embeddings.

- `collection_name`: Name of the Qdrant collection to search
- `query_text`: The search query in natural language
- `limit`: Maximum number of results to return (default: 5)
- `model`: OpenAI embedding model to use (default: text-embedding-3-small)

### list_collections

List all available collections in the Qdrant database.

### collection_info

Get information about a specific collection.

- `collection_name`: Name of the collection to get information about

## Example Usage in Claude Desktop

Once installed in Claude Desktop, you can use the tools like this:

```
What collections are available in my Qdrant database?

Search for documents about climate change in my "documents" collection.

Show me information about the "articles" collection.
```
fun-args 0.0.3
Tiny CLI argument parser inspired by Typer
Requires Python: >=3.9

# Fun Args Package

Simple argument passing library inspired by Typer

```python
from fun_args import argumentize

# Your main with explicitly defined parameters
def main(name: str, age: int = 25, smart = False):
    print(f"Hi, {name}")

# Don't forget to call my function
argumentize(main)
```

Now you in your terminal you can run:
`python filename.py --name Max --age 22`

Supports both flagged and ordered parameters. E.g.

```python
def main(a, b):
    print(a + b)

```

You could run in with
`python filename.py 2 7`

And get 27, because by default everything's converted to strings