semvec/pypi-packages huggingface dataset:
evraz-knime-clients 0.0.2
Библиотека интеграций для KNIME
Requires Python: >=3.10

### Knime Clients
Библиотека дает класс для запросов к SAP.

### Пример использования
```
from evraz.knime_clients import create_sap_client

sap = create_sap_client(
    user='user',
    password=password1!',
    cache_path='metadata/',
)

report = sap.get_report_avl(
    idrep=1,
    month=3,
    year=2025,
)
```
### Сборка
```
python3 -m build
python3 -m twine upload --repository testpypi dist/*
```
minium-mcp-server 0.1.1
A MCP server project
Requires Python: >=3.12

# minium-mcp-server MCP server

A MCP server project

## Components

### Resources

The server implements a simple note storage system with:
- Custom note:// URI scheme for accessing individual notes
- Each note resource has a name, description and text/plain mimetype

### Prompts

The server provides a single prompt:
- summarize-notes: Creates summaries of all stored notes
  - Optional "style" argument to control detail level (brief/detailed)
  - Generates prompt combining all current notes with style preference

### Tools

The server implements one tool:
- add-note: Adds a new note to the server
  - Takes "name" and "content" as required string arguments
  - Updates server state and notifies clients of resource changes

## Configuration

[TODO: Add configuration details specific to your implementation]

## Quickstart

### Install

#### Claude Desktop

On MacOS: `~/Library/Application\ Support/Claude/claude_desktop_config.json`
On Windows: `%APPDATA%/Claude/claude_desktop_config.json`

<details>
  <summary>Development/Unpublished Servers Configuration</summary>
  ```
  "mcpServers": {
    "minium-mcp-server": {
      "command": "uv",
      "args": [
        "--directory",
        "--path--/minium_mcp_server/server",
        "run",
        "minium-mcp-server",
        "--path",
        "--project-path--"
      ]
    }
  }
  ```
</details>

<details>
  <summary>Published Servers Configuration</summary>
  ```
  "mcpServers": {
    "minium-mcp-server": {
      "command": "uvx",
      "args": [
        "minium-mcp-server"
      ]
    }
  }
  ```
</details>

## Development

### Building and Publishing

To prepare the package for distribution:

1. Sync dependencies and update lockfile:
```bash
uv sync
```

2. Build package distributions:
```bash
uv build
```

This will create source and wheel distributions in the `dist/` directory.

3. Publish to PyPI:
```bash
uv publish
```

Note: You'll need to set PyPI credentials via environment variables or command flags:
- Token: `--token` or `UV_PUBLISH_TOKEN`
- Or username/password: `--username`/`UV_PUBLISH_USERNAME` and `--password`/`UV_PUBLISH_PASSWORD`

### Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging
experience, we strongly recommend using the [MCP Inspector](https://github.com/modelcontextprotocol/inspector).


You can launch the MCP Inspector via [`npm`](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm) with this command:

```bash
npx @modelcontextprotocol/inspector uv --directory --path--/minium_mcp_server/server run minium-mcp-server --path --project-path--
```


Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.
topsis-102203883 0.3.5
a package to calculate topsis

# topsis python package
a python package to choose the best alternative from a finite set of decision alternatives using TOPSIS.

## overview
the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) library provides an implementation of the TOPSIS decision-making method. this technique is used to rank alternatives based on multiple criteria by comparing their distance from an ideal best and an ideal worst solution.

## features
<ul>
  <li>normalize decision matrices</li>
  <li>apply weights and impacts to criteria</li>
  <li>calculate performance scores and rankings</li>
</ul>

## usage 
```from topsis_102203883 import main```

## sample 
```python <your_file> <input_file> <weights> <impacts>```
volcengine-knowledgebase-mcp 0.1.0
Add your description here
Requires Python: >=3.13

# Volcengine Knowledge Base MCP

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Python Version](https://img.shields.io/badge/python-3.10%2B-blue)](https://www.python.org/downloads/)

Claude Desktop 的火山引擎知识库 MCP 服务器，提供知识库搜索和对话功能。

## 特性

- 🔍 知识库搜索
- 💬 对话补全
- 🔐 安全的凭证管理
- 🚀 简单易用的配置

## 快速开始

### 前提条件

- Python 3.10 或更高版本
- Claude Desktop
- 你需要注册一个火山引擎账号，开通知识库服务，并且创建一个知识库

### 安装

1. 克隆仓库：
```bash
git clone git@github.com:suqidan/volc-kb-mcp.git
cd volc-kb-mcp
```

2. 安装依赖：
```bash
pip install -r requirements.txt
```

### 配置 Claude Desktop

编辑 Claude Desktop 配置文件：

- macOS: `~/Library/Application Support/Claude/claude_desktop_config.json`
- Windows: `%APPDATA%\Claude\claude_desktop_config.json`

添加以下配置：

```json
{
    "mcpServers": {
        "kb": {
            "command": "python",
            "args": [
                "/path/to/kb_mcp.py"
            ]
        }
    }
}
```

### 使用方法
设置好 Claude MCP JSON 配置后，你只需要直接告诉 Claude 你要使用知识库，它就会询问你添加对应的信息，包含 ak, sk, account_id 以及 collection_namme

1. 首次使用时配置凭证：
```python
await configure(
    access_key="your_access_key",
    secret_key="your_secret_key",
    account_id=your_account_id,
    collection_name="your_collection_name"
)
```

2. 搜索知识库：
```python
result = await search_knowledge("你的查询")
```

3. 对话补全：
```python
messages = [
    {"role": "system", "content": "你是一个有帮助的助手"},
    {"role": "user", "content": "你好"}
]
response = await chat_completion(messages)
```

## API 文档

### configure
配置火山引擎凭证和知识库设置。

**参数：**
- `access_key` (str): 火山引擎访问密钥
- `secret_key` (str): 火山引擎安全密钥
- `account_id` (int): 火山引擎账户ID
- `collection_name` (str): 知识库集合名称

### search_knowledge
搜索知识库。

**参数：**
- `query` (str): 搜索查询字符串

### chat_completion
获取对话补全响应。

**参数：**
- `messages` (List[dict]): 对话消息列表
- `stream` (bool, 可选): 是否使用流式响应
- `temperature` (float, 可选): 温度参数

## 安全性

- 所有凭证都安全存储在用户本地
- 配置文件权限仅限当前用户访问
- 不会将凭证发送到火山引擎 API 之外的任何地方

## 故障排除

如果遇到问题：

1. 确认凭证信息正确
2. 检查配置文件权限
3. 查看 Claude Desktop 日志文件
4. 如果需要重新配置，删除 `~/.config/volc_kb_mcp/config.json`

## 贡献

欢迎提交 Pull Requests！对于重大更改，请先开 issue 讨论您想要更改的内容。

## 许可证

本项目采用 MIT 许可证 - 详见 [LICENSE](LICENSE) 文件

## 作者

suqidan

## 致谢

- [火山引擎](https://www.volcengine.com/)
- [Claude Desktop](https://claude.ai/)
- [Model Context Protocol](https://modelcontextprotocol.io/)
pdf2png-mcp-server 0.1.0
Add your description here
Requires Python: >=3.13

# PDF to PNG MCP Server

A Model Context Protocol (MCP) server that provides PDF to PNG conversion capabilities. This server allows you to convert PDF documents into PNG images with a simple MCP tool call.

## Prerequisites

This server requires the Model Context Protocol (MCP). If you're new to MCP, start by installing the SDK:
```bash
uv pip install mcp
```

Additional requirements:
- Python 3.10 or higher
- [uv](https://github.com/astral-sh/uv) package manager
- poppler (required for pdf2image)

### Installing Poppler

- **Windows**: Download and install from [poppler-windows](https://github.com/oschwartz10612/poppler-windows/releases/)
- **macOS**: `brew install poppler`
- **Linux**: `sudo apt-get install poppler-utils`

## Installation

1. Clone this repository:
   ```bash
   git clone https://github.com/truaxki/mcp-Pdf2png.git
   cd mcp-Pdf2png
   ```

2. Create and activate a virtual environment:
   ```bash
   uv venv
   # Windows
   .venv\Scripts\activate
   # Unix/macOS
   source .venv/bin/activate
   ```

3. Install the package:
   ```bash
   uv pip install -e .
   ```

## Usage

### 1. Configure MCP Client

Add the server configuration to your `claude_desktop_config.json`. The file is typically located in:
- Windows: `%APPDATA%\Claude Desktop\config\claude_desktop_config.json`
- macOS/Linux: `~/.config/Claude Desktop/config/claude_desktop_config.json`

```json
{
  "mcpServers": {
    "pdf2png": {
      "command": "uv",
      "args": [
        "--directory",
        "/absolute/path/to/mcp-Pdf2png",
        "run",
        "pdf2png"
      ]
    }
  }
}
```

Note: Replace `/absolute/path/to/mcp-Pdf2png` with the actual path where you cloned the repository.

### 2. Using the Server

The server provides a single tool `pdf2png` with these parameters:
- `read_file_path`: Absolute path to the input PDF file
- `write_folder_path`: Absolute path to the directory where PNG files should be saved

Output:
- Each PDF page is converted to a PNG image
- Files are named `page_1.png`, `page_2.png`, etc.
- Returns a success message with the conversion count

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.