semvec/pypi-packages huggingface dataset:
arxiv-appendix-extractor 0.1.0
A tool to download arXiv papers, intelligently extract appendices with math formulas, and manage source files.
License: MIT
Requires Python: >=3.8

# arXiv Appendix Extractor

A powerful command-line tool to download LaTeX source files for arXiv papers, intelligently extract appendix sections, and clean up source files for papers without appendices.

This tool is ideal for researchers and data scientists who need to programmatically analyze the mathematical derivations and proofs often found in the appendices of scientific papers.

## Features

-   **Download by Date or ID**: Fetch papers from a specific date and category, or by a list of individual arXiv IDs.
-   **Intelligent Appendix Extraction**: Uses a hybrid approach:
    1.  **HTML-Assisted**: Prefers high-quality HTML from ar5iv.org for accurate section detection.
    2.  **Heuristic Fallback**: If HTML is unavailable, it uses robust heuristics to find appendices in the raw LaTeX source.
-   **Robust and Resumable**: Maintains a progress file to automatically resume interrupted jobs, saving time and bandwidth.
-   **Parallel Processing**: Leverages multiple CPU cores to process papers in parallel, significantly speeding up large jobs.
-   **Smart Cleanup**: Includes a separate utility to safely remove the source files of papers that were found to have no appendix, saving disk space.
-   **Easy to Use**: Packaged as a simple command-line tool.

## Installation

```bash
# (After you build and upload to PyPI)
pip install arxiv-appendix-extractor
```

## Usage

### Main Extractor Tool

The main tool is `arxiv-extractor`. You must provide either `--date` or `--ids`.

**1. Process papers from a specific date:**

This command will process the first 10 papers from the 'cs' (Computer Science) category on August 15, 2025, using 4 parallel workers.

```bash
arxiv-extractor --date 2025-08-15 --category cs --max-papers 10 --workers 4
```

**2. Process specific papers by ID:**

```bash
arxiv-extractor --ids 2310.06825 2403.00318
```

All results and intermediate files will be saved in the `pipeline_output_refactored/` directory by default.

### Cleanup Tool

After running the extractor, you can clean up the source files for papers that had no appendices.

The `arxiv-cleanup` tool requires the report file and the papers directory generated by the extractor.

**1. Perform a "dry run" (highly recommended first):**

This will show you which directories would be deleted, without actually deleting anything.

```bash
arxiv-cleanup \
  --report-file pipeline_output_refactored/final_appendix_results.json \
  --papers-dir pipeline_output_refactored/papers \
  --dry-run
```

**2. Execute the cleanup:**

This will prompt you for confirmation before deleting files.

```bash
arxiv-cleanup \
  --report-file pipeline_output_refactored/final_appendix_results.json \
  --papers-dir pipeline_output_refactored/papers
```

To skip the confirmation prompt (e.g., in a script), add the `-y` flag.
mteam 0.1.5
M-TEAM API SDK
License: MIT
Requires Python: >=3.7

# M-TEAM API SDK

M-TEAM API 的 Python SDK，提供了简单易用的接口封装。

## 安装

```bash
pip install mteam
```

## 快速开始

1. 配置环境变量
```bash
cp .env.example .env
```

编辑 `.env` 文件，填入你的 API 密钥：
```
MTEAM_API_KEY=your_api_key_here
MTEAM_BASE_URL=http://test2.m-team.cc/api
```

2. 基本使用
```python
from mteam import MTeamClient

# 创建客户端实例
client = MTeamClient()

# 搜索种子
from mteam.torrent import TorrentSearch, TorrentCategory
search = TorrentSearch(
    keyword="测试",
    categories=[TorrentCategory.MOVIE],
    page_size=10
)
result = client.torrent.search(search)

# 获取用户资料
profile = client.member.get_profile()
```

## 功能模块

### 用户模块
```python
from mteam.member import LoginForm, RegisterForm

# 用户登录
form = LoginForm(username="test", password="password")
result = client.member.login(form)

# 获取用户资料
profile = client.member.get_profile()
```

### 种子模块
```python
from mteam.torrent import TorrentSearch, TorrentCategory

# 搜索种子
search = TorrentSearch(keyword="测试")
results = client.torrent.search(search)

# 获取种子详情
torrent = client.torrent.get_detail(torrent_id=123)
```

### 字幕模块
```python
from mteam.subtitle import SubtitleSearch

# 搜索字幕
search = SubtitleSearch(keyword="测试")
subtitles = client.subtitle.search(search)

# 上传字幕
form = SubtitleUploadForm(
    title="测试字幕",
    torrent_id=123,
    language_id=1
)
result = client.subtitle.upload(form, "path/to/subtitle.srt")
```

## 测试

1. 安装测试依赖
```bash
pip install pytest python-dotenv
```

2. 运行测试
```bash
# 运行所有测试
pytest

# 运行特定模块测试
pytest tests/test_member.py
pytest tests/test_torrent.py

# 运行单个测试
pytest tests/test_member.py::test_login

# 显示详细输出
pytest -v
```

## 注意事项

1. API 密钥安全
   - 不要在代码中硬编码 API 密钥
   - 使用环境变量或配置文件管理密钥
   - 不要将密钥提交到版本控制系统

2. 错误处理
   - 所有 API 调用都可能抛出 `APIError` 异常
   - 检查响应的 `code` 和 `message` 字段
   - 合理处理网络错误和超时

3. 请求限制
   - 注意 API 的请求频率限制
   - 合理设置超时时间
   - 必要时实现请求重试机制

## 常见问题

1. API 密钥无效
   - 检查 `.env` 文件中的 `MTEAM_API_KEY` 是否正确
   - 确认 API 密钥未过期

2. 请求失败
   - 检查网络连接
   - 确认 API 基础 URL 是否正确
   - 查看错误响应的具体信息

3. 类型错误
   - 确保传入参数符合类型要求
   - 使用 IDE 的类型提示功能
   - 参考 API 文档中的参数说明

## API 文档

完整的 API 文档请参考：[API 文档链接]

## 贡献指南

1. Fork 本仓库
2. 创建功能分支
3. 提交代码
4. 创建 Pull Request

## 许可证

MIT License
universal-mcp-fpl 0.1.3
A Universal MCP Application: universal_mcp_fpl
License: MIT
Requires Python: >=3.11

# Fpl Universal MCP Server

This repository contains an implementation of an Fpl Universal MCP (Model Context Protocol) server. It provides a standardized interface for interacting with Fpl's tools and services through a unified API.

The server is built using the Universal MCP framework.

This implementation follows the MCP specification, ensuring compatibility with other MCP-compliant services and tools.

## Usage

You can start using Fpl directly from [agentr.dev](https://agentr.dev). Visit [agentr.dev/apps](https://agentr.dev/apps) and enable Fpl.

If you have not used universal mcp before follow the setup instructions at [agentr.dev/quickstart](https://agentr.dev/quickstart)

## Available Tools

The full list of available tools is at [Tools](./src/universal_mcp_fpl/README.md)

## Local Development

### 📋 Prerequisites

Ensure you have the following before you begin:

- **Python 3.11+** (recommended)
- **[uv](https://github.com/astral-sh/uv)** (install globally with `pip install uv`)

### 🛠️ Setup Instructions

Follow the steps below to set up your development environment:

1. **Sync Project Dependencies**

   ```bash
   uv sync
   ```

   This installs all dependencies from `pyproject.toml` into a local virtual environment (`.venv`).

2. **Activate the Virtual Environment**

   For Linux/macOS:

   ```bash
   source .venv/bin/activate
   ```

   For Windows (PowerShell):

   ```powershell
   .venv\Scripts\Activate
   ```

3. **Start the MCP Inspector**

   ```bash
   mcp dev src/universal_mcp_fpl/server.py
   ```

   This will start the MCP inspector. Make note of the address and port shown in the console output.

4. **Install the Application**
   ```bash
   mcp install src/universal_mcp_fpl/server.py
   ```

## 📁 Project Structure

```text
.
├── src/
│   └── universal_mcp_fpl/
│       ├── __init__.py       # Package initializer
│       ├── server.py         # Server entry point
│       ├── app.py            # Application tools
│       └── README.md         # List of application tools
├── tests/                    # Test suite
├── .env                      # Environment variables for local development
├── pyproject.toml            # Project configuration
└── README.md                 # This file
```

## 📄 License

This project is licensed under the [MIT License](LICENSE).

---

_Generated with **MCP CLI** — Happy coding! 🚀_