semvec/pypi-packages huggingface dataset:
spacelink 0.1.11
Space link budget calculation package
Requires Python: >=3.11

# SpaceLink

A Python library for radio frequency calculations, including antenna modeling, RF 
conversions, and noise calculations.

Created and maintained by [Cascade Space](https://cascade.space).

Published documentation for the latest released version can be found here: 
https://cascade-space-co.github.io/spacelink/

## Features

- **Antenna Modeling**: Calculate antenna gain, beamwidth, and polarization effects
- **RF System Analysis**: Model complete RF chains with cascaded elements
- **Link Budget Calculations**: Comprehensive analysis of radio communication links
- **Noise Calculations**: System noise temperature and related parameters
- **Space Communications**: Built-in support for satellite link analysis
- **Unit-Aware Calculations**: Integrated unit handling for RF parameters

## Installation

### Quick Install

For users who want to import a released version of the package:
```bash
pip install spacelink
```

### Development Setup

Aside from modifying the source code you may want to install from source in order to:

* Run the provided Jupyter notebooks
* Generate the documentation locally for a specific version


#### Prerequisites

1. Python 3.11 or higher
2. Poetry package manager ([Install Poetry](https://python-poetry.org/docs/))

#### Getting Started

1. Clone the repository:
   ```bash
   git clone https://github.com/cascade-space-co/spacelink.git
   cd spacelink
   ```

2. Install it to a Poetry virtual environment using one of these options:
   
   * Production mode without developer tools:
     ```bash
     poetry install
     ```
   
   * With developer tools:
     ```bash
     poetry install --with dev
     ```

   * With Jupyter notebook dependencies:
     ```bash
     poetry install --with demo
     ```

   * With developer tools and Jupyter notebook dependencies:
     ```bash
     poetry install --with dev,demo
     ```

## Documentation

The documentation includes API references and technical guides.

To build the documentation locally:
```bash
poetry run sphinx-build -b html docs/source docs/build/html
```

Then open `docs/build/html/index.html` in your browser.

## Contributing

We welcome contributions to the SpaceLink project! See 
[CONTRIBUTING.md](https://github.com/cascade-space-co/spacelink/blob/main/CONTRIBUTING.md) for detailed instructions and guidelines.

## License

[MIT License](https://github.com/cascade-space-co/spacelink/blob/main/LICENSE)
iflow-mcp_vonequinox-websearchmcp 0.1.0
MCP (Model Context Protocol) web search server (Brave search + fetch tools)
Requires Python: >=3.10

#  WebSearch MCP Server

一个基于 MCP (Model Context Protocol) 的智能网页搜索服务器，支持 AI 增强搜索、Brave 搜索和网页抓取功能。

> **推荐**: 本项目推荐使用 `SOTASearch.py`，它结合了 AI 深度搜索和 Brave 搜索，**并行执行**两种搜索以提升速度。适合与 [CherryStudio](https://github.com/kangfenmao/cherry-studio) 配合使用。

## 功能

| 工具 | 说明 |
|------|------|
| `web_search` | AI 深度搜索 + Brave 搜索（并行执行），返回链接列表和 AI 总结 |
| `fetch_html` | 抓取网页 HTML 内容 |
| `fetch_text` | 抓取网页并提取纯文本 |
| `fetch_metadata` | 抓取网页元数据（标题、描述、链接） |

## 安装

### 1. 克隆项目

```bash
git clone https://github.com/yourusername/WebSearchMCP.git
cd WebSearchMCP
```

### 2. 安装依赖

**使用 uv（推荐）：**

```bash
uv sync
```

**使用 pip：**

```bash
pip install -r requirements.txt
```

### 3. 配置环境变量

复制 `.env.example` 为 `.env` 并填入配置：

```bash
cp .env.example .env
```

主要配置项：

```env
# 代理配置（可选）
PROXY=http://127.0.0.1:7890
CF_WORKER=https://your-worker.workers.dev

# OpenAI API 配置（SOTASearch 必需）
OPENAI_API_KEY=sk-xxx
OPENAI_BASE_URL=https://api.openai.com/v1
OPENAI_MODEL=gpt-4o
```

## 快速开始

```bash
# 使用 uv（推荐）
uv run SOTASearch.py

# 或使用 python
python SOTASearch.py
```

## 配置 CherryStudio

在 CherryStudio 的 MCP 服务器设置中添加（必须使用虚拟环境中的 Python 绝对路径）：

**Windows 示例：**

```json
{
  "mcpServers": {
    "sota-search": {
      "name": "Websearch",
      "type": "stdio",
      "command": "D:\\Code\\github\\WebSearchMCP\\.venv\\Scripts\\python.exe",
      "args": ["D:/Code/github/WebSearchMCP/SOTASearch.py"]
    }
  }
}
```

**macOS/Linux 示例：**

```json
{
  "mcpServers": {
    "sota-search": {
      "name": "Websearch",
      "type": "stdio",
      "command": "/path/to/WebSearchMCP/.venv/bin/python",
      "args": ["/path/to/WebSearchMCP/SOTASearch.py"]
    }
  }
}
```

> **注意**: 推荐使用项目虚拟环境中的 Python 解释器（`.venv/Scripts/python.exe` 或 `.venv/bin/python`），确保依赖正确加载。系统 Python 也可以使用，但需确保已安装所有依赖。

> **提示**: 代理和 API 配置建议写在 `.env` 文件中，无需在命令行参数中指定。

## 命令行参数

| 参数 | 说明 | 示例 |
|------|------|------|
| `--proxy` | 本地代理地址 | `--proxy http://127.0.0.1:7890` |
| `--cf-worker` | Cloudflare Worker 地址 | `--cf-worker https://xxx.workers.dev` |
| `--openai-api-key` | OpenAI API Key | `--openai-api-key sk-xxx` |
| `--openai-base-url` | OpenAI API Base URL | `--openai-base-url https://api.openai.com/v1` |
| `--openai-model` | 模型名称（默认 gpt-4o） | `--openai-model gpt-4o` |

## 工具说明

### web_search

AI 深度搜索 + Brave 搜索（并行执行）。

参数：
- `query`: 搜索关键词（必填）
- `max_results`: 返回的最大结果数，默认 10

返回格式：

```json
{
  "success": true,
  "query": "搜索关键词",
  "links": [
    {"title": "标题", "url": "链接", "description": "描述"}
  ],
  "ai_summary": "AI 总结内容"
}
```

### fetch_html

抓取网页的原始 HTML 内容。

参数：
- `url`: 目标网址（必填）
- `headers`: 可选的请求头

### fetch_text

抓取网页并提取纯文本内容（去除 HTML 标签）。

参数：
- `url`: 目标网址（必填）
- `headers`: 可选的请求头

### fetch_metadata

抓取网页的元数据信息。

参数：
- `url`: 目标网址（必填）
- `headers`: 可选的请求头

返回：标题、描述、页面链接列表

## 特性

- AI 深度搜索 + Brave 搜索**并行执行**
- 支持本地代理 (`--proxy`)
- 支持 Cloudflare Worker 代理 (`--cf-worker`)
- 自动内容截断，防止响应过大
- 支持 `.env` 文件配置

## 依赖

- Python 3.10+
- fastmcp
- beautifulsoup4
- curl_cffi
- lxml
- openai
iflow-mcp_sevalla-templates-python-demo-mcp-server 0.1.0
A demonstration MCP server implementing Server-Sent Events (SSE)
Requires Python: >=3.11

# Python Demo MCP Server

## Introduction
This is a demonstration server implementing the Model Context Protocol (MCP) with Server-Sent Events (SSE). It provides a practical example of how to build a server that can handle streaming content production and management.

## Getting Started

1. Set up your Python environment

```bash
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
```

2. Install the required packages

```bash
uv pip install -r requirements.txt
```

## Run the server

### Development Environment

```bash
python server.py
```

The server will start on http://localhost:8080 by default.

### Production Environment

For deployment on Sevalla, make sure to select Dockerfile based build environment!

## Usage in Cursor

To use this server in Cursor, paste the following in your `mcp.json` file:

```json
"demo-mcp": {
  "url": "https://<your-mcp-server-domain>/sse"
}
```

Make sure to replace `<your-mcp-server-domain>` with the actual domain of your server.