semvec/pypi-packages huggingface dataset:
yinzhou-bark-mcp-server 0.1.4
Bark MCP Server for sending notifications to Apple devices
License: MIT
Requires Python: >=3.6
Keywords: bark, notification, mcp, server, apple

# yinzhou-bark-mcp-server

Bark MCP Server 是一个用于向苹果设备发送通知的 MCP 服务，支持通过 HTTP 接口发送各种类型的 Bark 通知。

## 功能特性

- ✅ 支持发送简单通知
- ✅ 支持发送带声音的通知
- ✅ 支持发送持续响铃的通知
- ✅ 支持发送带自定义图标的通知（需要 iOS 15+）
- ✅ 支持发送带分组的通知
- ✅ 支持发送加密通知
- ✅ 支持发送重要警告
- ✅ 支持发送带 URL 的通知
- ✅ 支持发送带图片的通知
- ✅ 支持发送带复制文本的通知
- ✅ 支持通过环境变量配置
- ✅ 提供健康检查接口

## 安装要求

- Python 3.6+
- pip 或 uv 包管理器

## 快速开始

### 方法 1: 使用 uvx 直接运行（推荐）

```bash
# 直接运行服务
uvx --from yinzhou-bark-mcp-server bark-mcp-server

# 带环境变量运行
BARK_SERVER_URL=http://yin520.cn:777 BARK_DEVICE_KEY=agyP4k9wG6mr99tTkez8qB PORT=5000 uvx --from yinzhou-bark-mcp-server bark-mcp-server
```

### 方法 2: 使用 pip 安装

```bash
# 安装包
pip install yinzhou-bark-mcp-server

# 运行服务
bark-mcp-server
```

## 环境变量配置

| 环境变量 | 默认值 | 说明 |
|---------|-------|------|
| BARK_SERVER_URL | http://yin520.cn:777 | Bark 服务器 URL |
| BARK_DEVICE_KEY | agyP4k9wG6mr99tTkez8qB | 设备密钥 |
| PORT | 5000 | 服务端口 |
| FLASK_ENV | production | Flask 运行环境 |

## API 接口

### 1. 发送通知

**请求 URL**: `POST /mcp`

**请求体**:
```json
{
  "action": "send_notification",
  "params": {
    "title": "通知标题",
    "body": "通知内容",
    "type": "simple",
    "sound": "minuet",
    "icon": "https://example.com/icon.jpg",
    "group": "default",
    "url": "https://example.com",
    "image": "https://example.com/image.jpg",
    "copy": "要复制的文本",
    "autoCopy": true
  }
}
```

**响应**:
```json
{
  "code": 200,
  "message": "发送成功",
  "data": {
    "code": 200,
    "message": "success"
  }
}
```

### 2. 健康检查

**请求 URL**: `GET /health`

**响应**:
```json
{
  "code": 200,
  "message": "服务正常",
  "data": {
    "status": "healthy",
    "service": "BarkMCPService"
  }
}
```

### 3. 服务信息

**请求 URL**: `GET /`

**响应**:
```json
{
  "code": 200,
  "message": "Bark MCP服务运行正常",
  "data": {
    "service": "Bark MCP Server",
    "version": "1.0.0",
    "endpoints": {
      "/mcp": "MCP服务接口（POST）",
      "/health": "健康检查接口（GET）"
    }
  }
}
```

## MCP 服务配置

在支持 MCP 服务的工具中，可以使用以下配置：

```json
{
  "mcpServers": {
    "Bark": {
      "command": "uvx",
      "args": [
        "--from",
        "yinzhou-bark-mcp-server",
        "bark-mcp-server"
      ],
      "env": {
        "BARK_SERVER_URL": "http://yin520.cn:777",
        "BARK_DEVICE_KEY": "agyP4k9wG6mr99tTkez8qB",
        "PORT": "5000"
      }
    }
  }
}
```

## 开发指南

### 克隆仓库

```bash
git clone https://github.com/yourusername/yinzhou-bark-mcp-server.git
cd yinzhou-bark-mcp-server
```

### 安装开发依赖

```bash
pip install -e "[dev]"
```

### 运行测试

```bash
pytest
```

### 构建项目

```bash
python -m build
```

## 许可证

MIT License
lyric-fetch-mcp 0.1.0
Add your description here
License: MIT
Requires Python: >=3.12
Keywords: mcp, fastmcp, lyrics, genius, llm

# lyric-fetch-mcp

A small MCP (Model Context Protocol) server that fetches song lyrics and artist/album song lists using the Genius API via `lyricsgenius` and exposes them as FastMCP tools.

## Overview ✅

- **Purpose:** Provide simple, reusable tools for retrieving song lyrics and lists of songs by artist/album for LLMs or other tooling that can call MCP tools.
- **Tools exposed:** `get_song_lyrics(song_title, artist_name)`, `get_artist_songs(artist_name, max_songs=5)`, `get_album_songs(album_title, artist_name)`
- **Server framework:** Uses `fastmcp` to register the tools and run the MCP server.

## Installation (after publishing to `uv`) 🔧

Once you publish this package to a registry that `uv` can reach (e.g., PyPI or a private index), users can install and run it locally with `uv`:

- Install the published package:

  - `uv add lyric-fetch-mcp`

- Run the installed tool directly (ephemeral environment):

  - `GENIUS_ACCESS_TOKEN="<your_token>" uvx lyric-fetch-mcp`

- Or add it as a dependency to your `uv` project and run:

  - `uv add lyric-fetch-mcp`
  - `uv run -- lyric-fetch-mcp`

> Note: `uvx` runs a tool in an ephemeral environment created by `uv` (similar to `pipx`).

## Running locally (developer)

- One-off run with an env var:

  - `GENIUS_ACCESS_TOKEN="your_token_here" python -m lyric_fetch_mcp.server`

- Install locally for development and run the script:

  - `python -m pip install -e .`
  - `lyric-fetch-mcp`  (after install)

## Environment

- Requires a valid `GENIUS_ACCESS_TOKEN` environment variable.
- For production/CI, store the token in your CI/host secret manager (GitHub Secrets, Docker secrets, etc.).

## CLI examples

- Fetch lyrics quickly from the shell (one-line):

  - `GENIUS_ACCESS_TOKEN="your_token" python -c 'from lyric_fetch_mcp.server import get_song_lyrics; print(get_song_lyrics("Sofia","Clairo"))'`

## Contributing & Publishing

- The project uses `uv_build` as the PEP 517 backend — build with `uv build` and publish with `uv publish`.
- Please follow the repository's contribution guidelines and add tests for any new behavior.

## License

- MIT — see `LICENSE` for details.
iflow-mcp_juliusolsson05-lazy-llms 0.1.0
LLM-Native Project Management MCP Server - A comprehensive project management system designed for LLM agents
License: MIT
Requires Python: >=3.8
Keywords: ai, llm, mcp, model-context-protocol, project-management

# LLM-Native Project Management System

A project management system designed for LLM agents as first-class citizens. Track issues, log work, and manage projects with rich context - all optimized for AI workflows.

## 🚀 Quick Start (2 minutes)

### One-Command Setup
```bash
make quickstart
```

This will:
1. Install all dependencies
2. Initialize the database
3. Start the web UI at http://localhost:1929
4. Copy Claude Desktop integration command to clipboard

### Manual Setup

```bash
# 1. Install dependencies
pip install -r requirements.txt
cd mcp && pip install -r requirements.txt && cd ..

# 2. Initialize database
make init-db

# 3. Start services
make start  # Starts all services
```

## 🎯 Core Features

- **Rich Issue Tracking** - Issues with comprehensive specs and technical approaches
- **Work Logging** - Track development activity with artifacts and context
- **Git Integration** - Automatic branch creation and commit formatting
- **Web UI** - Visual dashboards and issue management at http://localhost:1929
- **MCP Tools** - 23 tools for LLM agents to manage projects

## 📋 Basic Usage

### Create an Issue
```bash
pm_create_issue --type feature --title "Add user auth" --description "..."
```

### Start Work
```bash
pm_start_work --issue-key LAZY-001
```

### Log Progress
```bash
pm_log_work --issue-key LAZY-001 --activity code --summary "Implemented JWT"
```

### View Status
```bash
pm_status  # Project overview
pm_list_issues  # All issues
```

## 🔧 Claude Desktop Integration

Add to `~/Library/Application Support/Claude/claude_desktop_config.json`:

```json
{
  "mcpServers": {
    "pm-server": {
      "command": "python",
      "args": ["/path/to/lazy-llms/mcp/src/server.py"],
      "env": {
        "PM_DATABASE_PATH": "/path/to/lazy-llms/data/jira_lite.db"
      }
    }
  }
}
```

## 📂 Project Structure

```
lazy-llms/
├── mcp/src/              # MCP server for LLM integration
├── src/jira_lite/        # Web UI and database
├── data/                 # Database files
├── scripts/              # Utility scripts
└── docs/                 # Documentation
```

## 🛠 Available Commands

- `make quickstart` - Complete one-command setup
- `make start` - Start all services
- `make stop` - Stop all services
- `make init-db` - Initialize database
- `make clean` - Clean generated files
- `make help` - Show all commands

## 📚 Documentation

- [Full Documentation](docs/LLM_NATIVE_PM_SPECIFICATION.md)
- [Deprecated Docs](docs/deprecated/) - Old documentation for reference

## 🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

## 📄 License

MIT License

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Built for AI agents, by AI agents 🤖