semvec/pypi-packages huggingface dataset:
groupdocs-signature-cloud 25.6
GroupDocs.Signature Cloud Python SDK
Keywords: groupdocs, signature, cloud, python, sdk

# GroupDocs.Signature Cloud Python SDK
Python package for communicating with the GroupDocs.Signature Cloud API

## Requirements

Python 2.7 or 3.4+

## Installation
Install `groupdocs-signature-cloud` with [PIP](https://pypi.org/project/pip/) from [PyPI](https://pypi.org/) by:

```sh
pip install groupdocs-signature-cloud
```

Or clone repository and install it via [Setuptools](http://pypi.python.org/pypi/setuptools): 

```sh
python setup.py install
```

## Getting Started

Please follow the [installation procedure](#installation) and then run following:

```python
# Import module
import groupdocs_signature_cloud

# Get your app_sid and app_key at https://dashboard.groupdocs.cloud (free registration is required).
app_sid = "XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX"
app_key = "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX"

# Create instance of the API
api = groupdocs_signature_cloud.InfoApi.from_keys(app_sid, app_key)

try:
    # Retrieve supported file-formats
    response = api.get_supported_file_formats()

    # Print out supported file-formats
    print("Supported file-formats:")
    for format in response.formats:
        print('{0} ({1})'.format(format.file_format, format.extension)) 
except groupdocs_signature_cloud.ApiException as e:
    print("Exception when calling get_supported_file_formats: {0}".format(e.message))
```

## Licensing
GroupDocs.Signature Cloud Python SDK licensed under [MIT License](http://github.com/groupdocs-signature-cloud/groupdocs-signature-cloud-python/LICENSE).

## Resources
+ [**Website**](https://www.groupdocs.cloud)
+ [**Product Home**](https://products.groupdocs.cloud/signature)
+ [**Documentation**](https://docs.groupdocs.cloud/signature/)
+ [**Free Support Forum**](https://forum.groupdocs.cloud/c/signature)
+ [**Blog**](https://blog.groupdocs.cloud/category/signature)

## Contact Us
Your feedback is very important to us. Please feel free to contact us using our [Support Forums](https://forum.groupdocs.cloud/c/signature).
slackle 0.2.7
A lightweight Slack bot framework for building modular and maintainable Slack bots.
Requires Python: >=3.10

<p align="center">
  <a href="https://slackle.dev">
    <picture>
      <source media="(prefers-color-scheme: dark)" srcset="https://slackle.dev/static/logo-light.png">
      <source media="(prefers-color-scheme: light)" srcset="https://slackle.dev/static/logo-dark.png">
      <img alt="Slackle" src="https://slackle.dev/static/logo-dark.png" width="500">
    </picture>
  </a>
</p>

<p align="center">
  <a href="https://slackle.dev"><img src="https://img.shields.io/badge/✨_Slackle-Magic_Framework-purple" alt="Slackle Framework"/></a>
  <a href="https://pypi.org/project/slackle/"><img src="https://img.shields.io/pypi/v/slackle?label=PyPI" alt="PyPI Version"/></a>
  <a href="https://slack.dev"><img src="https://img.shields.io/badge/built_with-Slack_SDK_+_FastAPI-4A154B?logo=slack" alt="Slack + FastAPI"/></a>
  <a href="https://www.python.org/"><img src="https://img.shields.io/badge/python-3.10+-yellow?logo=python" alt="Python Version"/></a>
</p>

---

## ✨ Slackle up your Slack!

[**Slackle**](https://slackle.dev) is a lightweight and magical framework for building Slack apps effortlessly.

[→ Visit the Official Website](https://slackle.dev)

---

## 🚀 Features

- ⚡️ **Slash command routing** — Handle `/your-command`, `/like`, `/magic`
- 💬 **Event handling** — `@app_mention`, `message`, `reaction_added`, etc.
- 🎨 **Slack formatting** — Clean markdown and block formatting made easy
- 🧩 **Plugin system** — Extend functionality with decorators and custom plugins
- ⚙️ **FastAPI + Slack SDK** — Built on proven tools, superfast and flexible

---

## 📦 Installation

```bash
pip install slackle
```

---

## 🧑‍💻 Getting Started

```python
import os

from slackle import Slackle, SlackleConfig
from slackle.utils.slack import get_user_mention

# Set up slackle configuration
config = SlackleConfig(
    app_token=os.getenv("APP_TOKEN"),
    verification_token=os.getenv("VERIFICATION_TOKEN")
)

# Initialize slackle app
app = Slackle(config=config)

# Add handler for message events
@app.on_event("message")
async def say_hello(slack, user_id, channel_id):
    mention = get_user_mention(user_id)
    print("User ID:", user_id)
    await slack.send_message(
        channel=channel_id,
        message=f"Hello {mention}!",
    )

# Add handler for /say slash command
@app.on_command("/say")
async def say_something(slack, text, user_id, channel_id):
    name = await slack.get_user_name(user_id)
    await slack.send_message(
        channel=channel_id,
        message=f"{name} said: {text}",
    )
```

---

> 📁 See [examples/](examples) for more code samples.

## 🤝 Contribution

We welcome contributions!
Check out the [Contributing Guide](CONTRIBUTING.md) to get started.

---

## 🪪 License

MIT License. See the [LICENSE](LICENSE) file for details.
ble-hrm-server 0.2.3
A Python MCP server for Bluetooth Low Energy (BLE) Heart Rate Monitoring. Connects to BLE HRM devices, monitors heart rate, and provides tools for heart rate data analysis and visualization.
Requires Python: <3.14,>=3.10
Keywords: ble, hrm, mcp, llm

# ble-hrm-server

A Python MCP server for Bluetooth Low Energy (BLE) Heart Rate Monitoring. Connects to BLE HRM devices, monitors heart rate, and provides tools for heart rate data analysis and visualization.

## Features
- Discover and connect to BLE Heart Rate Monitor (HRM) devices
- Monitor heart rate in real time
- Retrieve current, average, and maximum heart rate
- Generate heart rate charts and time-bucketed data
- Store and manage heart rate data

## Installation

We recommend using a virtual environment and `uv` for dependency management:

```bash
python3 -m venv .venv
source .venv/bin/activate
pip install uv
uv sync
```

Or install directly from PyPI:

```bash
pip install ble-hrm-server
```

## Usage

### Run the server

If installed from source:
```bash
uv run src/hrm/server.py
```

If installed from PyPI:
```bash
uvx ble-hrm-server
```

### Run tests
```bash
uv run pytest
```

## Configuration

The server can be configured via a `.env` file. Use `.env.sample` as a template. Required environment variables:
- `QINIU_ACCESS_KEY`: Your Qiniu access key
- `QINIU_SECRET_KEY`: Your Qiniu secret key
- `QINIU_BUCKET_NAME`: Qiniu bucket name
- `QINIU_BUCKET_DOMAIN`: Qiniu bucket domain

## Tools

- **monitoring_heart_rate**: Start monitoring heart rate for a specified duration
- **get_heart_rate**: Get the average heart rate over the last 10 seconds
- **evaluate_active_heart_rate**: Get the maximum heart rate in the last 60 seconds
- **get_heart_rate_bucket**: Get heart rate data in 1-second buckets for the last 10 seconds
- **build_heart_rate_chart**: Generate a PNG chart of heart rate data for the last 600 seconds

## License
MIT

## Sponsor
Special thanks to Qiniu Cloud for providing storage and LLM inference services.
TaskMcpClient 0.1.53
Localization configurable MCP client, interacting through task files
Requires Python: >=3.10

# TaskMcpClient 文件任务型MCP客户端工具

# 1 设计目的
在各类AI聊天桌面工具（如[Claude for Desktop](https://support.anthropic.com/en/articles/10065433-installing-claude-for-desktop)、[Cursor](https://www.cursor.com/cn/downloads)、[Trae](https://www.trae.com.cn/home)、[CherryStuio](https://www.cherry-ai.com/) ）中，可以方便地配置LLM模型服务和各类MCP服务端，实现LLM+MCP结合的智能体构建与测试。
但测试通过后，就可能需要部署在服务器上，在LLM和MCP服务端配置妥当情况下，让程序自动执行自然语言描述的任务。由程序控制任务指令，并取走任务执行结果。

这里构想这样的任务，请求不会太频繁，大约是分钟级。采用某个目录中的Markdown文件作为输入，采用另外目录作为输出。

这就是“文件任务型MCP客户端工具”，简称“TaskMcpClient”。

另外，TaskMcpClient作为一个通用工具，从当前工作目录读取LLM和MCP配置，不依赖于其它环境和工具。


# 2 安装指南

在Windows或Linux系统中，执行如下命令
```
uv tool install TaskMcpClient
```
即可得到 TaskMcpClient 可执行文件，对应目录分别是
- 在Windows下，`%HOMEPATH%\.local\bin\TaskMcpClient.exe`
- 在Linux下，`$HOME/.local/bin/TaskMcpClient`

# 3 配置指南

1. 创建新的工作目录，假定是`<TaskDir>`
2. 在终端窗口进入到 `<TaskDir>`
3. 执行 TaskMcpClient 命令，可以看到该程序的运行日志，并默认进入聊天状态。此时由于没有配置LLM，会出现`APIConnectionError`错误，输入`quit`退出命令

## 3.1 配置LLM
编辑 `<TaskDir>/.env`文件，以Ollama模型为例，配置如下：
```
OPENAI_BASE_URL=http://127.0.0.1:11434/v1
OPENAI_API_KEY=ollama
OPENAI_MODEL=deepseek-r1
```

## 3.2 配置MCP

### 3.2.1 配置运行MCP服务端的命令路径
编辑 `<TaskDir>/mcp.server/cmdPath.env`文件，以Window环境为例，配置如下：

```
uv=C:\Python3.11\Scripts\uv.exe    # uv run 按指定目录下.venv创建临时环境运行
uvx=C:\Python3.11\Scripts\uvx.exe  # uvx 等同于 uv tool run，运行Python包内命令
bun=C:\Users\Administrator\.bun\bin\bun.exe  # js运行时，内置了打包器、转译器、任务运行器和 npm 客户端
bunx=C:\Users\Administrator\.bun\bin\bunx.exe  # bunx 是 bun x 的别名
python=C:\Python3.11\python.exe
```

### 3.2.2 配置MCP服务端
采用json格式，一个MCP服务端一个json文件

#### 3.2.2.1 stdio示例
- `weather.stdio.json`
```
{
    "command": "uv",
    "args": [
        "--directory",
        "E:/Full/Path/McpServer/weather",
        "run",
        "weather.py"
    ],
    "env": {}
}
```
#### 3.2.2.2 sse示例
- `test_xuanyuan.sse.json`
```
{
    "url": "http://127.0.0.1:8000/sse",
    "headers": {}
}
```

#### 3.2.2.3 websocket

# 4 运行指南
## 4.1 交互聊天
直接运行 TaskMcpClient 命令
```
TaskMcpClient
```

## 4.2 任务执行
直接运行 TaskMcpClient 命令
```
TaskMcpClient task
```

# 5 配置任务