semvec/pypi-packages huggingface dataset:
mcp-server-canteen 0.2.8
MCP Server for Canteen Data
Requires Python: >=3.10
Keywords: api, canteen, mcp

# 餐厅数据 MCP Server

本服务用于查询职工餐厅就餐人数数据，提供指定日期范围内的早餐和午餐就餐人数统计。

docs: https://modelcontextprotocol.io/quickstart/server

## 安装

### 通过 pip 安装

```bash
pip install mcp-server-canteen
```

### 通过 uv 安装

```bash
uv pip install mcp-server-canteen
```

### 开发环境安装

```bash
git clone https://github.com/wrdan/mcp-server-canteen.git
cd mcp-server-canteen
uv pip install -e .
```

## 环境变量配置

在使用服务之前，需要配置以下环境变量：

- `CANTEEN_API_TOKEN`: API认证令牌
- `CANTEEN_API_BASE`: API基础URL

### 环境变量获取方式

1. 联系系统管理员获取 API 认证令牌
2. API基础URL通常由系统管理员提供

### 设置环境变量

#### Windows
```bash
set CANTEEN_API_TOKEN=your_token
set CANTEEN_API_BASE=your_base_url
```

#### Linux/Mac
```bash
export CANTEEN_API_TOKEN=your_token
export CANTEEN_API_BASE=your_base_url
```

## 服务运行

### 使用 uv 运行

```bash
uv run mcp-server-canteen
```

### 使用 Python 运行

```bash
python -m mcp_server_canteen.server
```

## 功能说明

服务提供以下功能：

- `get_canteen_data`: 查询指定日期范围内的餐厅就餐人数数据
  - 参数: 
    - `start_date`: 开始日期，格式为YYYYMMDD（如20250331）
    - `end_date`: 结束日期，格式为YYYYMMDD（如20250331）
    - `period`: 相对时间范围，可选值：
      - `today`: 今天
      - `yesterday`: 昨天
      - `day_before_yesterday`: 前天
      - `this_week`: 本周
      - `last_week`: 上周
      - `this_month`: 本月
      - `last_month`: 上月
  - 返回: 包含早餐人数、午餐人数和总计人数的文本统计

## 使用 Claude for Desktop 作为客户端测试

### 配置

打开并编辑文件： ~/Library/Application\ Support/Claude/claude_desktop_config.json，内容如下：

<details>
<summary>使用 uvx</summary>

```json
"mcpServers": {
  "canteen": {
    "command": "uvx",
    "args": ["mcp-server-canteen"],
    "env": {
        "CANTEEN_API_TOKEN": "CANTEEN_API_TOKEN",
        "CANTEEN_API_BASE": "ANTEEN_API_BASE"
    }
  }
}
```
</details>

<details>
<summary>使用 uv</summary>

```json
"mcpServers": {
  "canteen": {
    "command": "uv",
    "args": ["run", "mcp-server-canteen"],
    "env": {
        "CANTEEN_API_TOKEN": "CANTEEN_API_TOKEN",
        "CANTEEN_API_BASE": "ANTEEN_API_BASE"
    }
  }
}
```
</details>

<details>
<summary>本地测试</summary>

```json
"mcpServers": {
  "canteen": {
    "command": "python",
    "args": ["-m", "mcp-server-canteen.server"],
    "env": {
        "CANTEEN_API_TOKEN": "CANTEEN_API_TOKEN",
        "CANTEEN_API_BASE": "ANTEEN_API_BASE"
    }
  }
}
```
</details>

### 重启 Claude for Desktop

重启不报错，且有`锤子`图标显示可用的 MCP Tool，即为成功；否则，查看日志排查

## 错误处理

### 常见错误及解决方案

1. **环境变量未设置**
   - 错误信息：`缺少必要的环境变量配置`
   - 解决方案：确保已正确设置所有必需的环境变量

2. **日期格式错误**
   - 错误信息：`日期格式不正确，请使用YYYYMMDD格式`
   - 解决方案：检查日期格式是否正确，例如：20240321

3. **API请求失败**
   - 错误信息：`HTTP请求失败` 或 `API返回错误`
   - 解决方案：
     - 检查网络连接
     - 验证API令牌是否正确
     - 确认API基础URL是否正确

4. **服务器连接失败**
   - 错误信息：`无法连接到MCP服务器`
   - 解决方案：
     - 确保服务器正在运行
     - 检查端口是否被占用
     - 验证配置文件是否正确

## Claude for Desktop 日志

日志文件夹：~/Library/Logs/Claude

来自具体 MCP Server 的日志：mcp-server-canteen.log

MCP 连接通用日志：mcp.log

## 工作原理

1. 客户将您的查询发送给 Claude
2. Claude 分析可用的工具并决定使用哪一个
3. 客户端通过 MCP 服务器执行所选工具
4. 结果被发回给 Claude
5. Claude 制定了自然语言响应
6. 答案已经展示给你了！
xai-agg 0.1.0
Rank based, multi-criteria aggregation method for explainable AI models.
Requires Python: >=3.10

<a id="readme-top"></a>

<!-- PROJECT LOGO -->
<br />
<div align="center">
  <a href="https://github.com/hiaac-finance/xai_aggregation">
    <img src="resources/logo-black.png" alt="Logo" height="150">
  </a>

  <p align="center">
    A python package for a rank based, multi-criteria aggregation method for explainable AI models.
    <div align="center">
      <img src="https://img.shields.io/github/repo-size/hiaac-finance/xai_aggregation" alt="Repo Size">
      <img src="https://img.shields.io/github/stars/hiaac-finance/xai_aggregation" alt="Stars">
      <img src="https://img.shields.io/github/license/hiaac-finance/xai_aggregation" alt="License">
      <img src="https://img.shields.io/readthedocs/xai-agg" alt="Documentation Status">
    </div>
    <br />
    <br />
    <a href="https://xai-agg.readthedocs.io/en/latest/">Read the Docs</a>
    ·
    <a href="">Paper</a>
  </p>
</div>

<!-- ABOUT THE PROJECT -->
## About

<div align="center">
    <img src="resources/diagram-black.png" alt="Diagram" style="border-radius: 15px;">
</div>

Explainability is crucial for improving the transparency of black-box machine learning models. With the advancement of explanation methods such as LIME and SHAP, various XAI performance metrics have been developed to evaluate the quality of explanations. However, different explainers can provide contrasting explanations for the same prediction, introducing trade-offs across conflicting quality metrics. Although available aggregation approaches improve robustness,
reducing explanations’ variability, very limited research employed a multi-criteria decision-making approach. To address this gap, <a href="">this package's paper</a> introduces a multi-criteria rank-based weighted aggregation method that balances multiple quality metrics simultaneously to produce an ensemble of explanation models.<!--  -->


<p align="right">(<a href="#readme-top">back to top</a>)</p>

## Installation
Run `pip install xai-agg` to install the package.
biomeai-nvidia 1.0.0
A package for easy integration with NVIDIA AI capabilities
Requires Python: >=3.6

# BiomeAI-NVIDIA

Simple Python package for NVIDIA's ColabFold MSA search functionality.

## Install
```bash
pip install biomeai-nvidia
```

## Quick Usage

```python
from biomeai import ColabFoldMSA

# Initialize with API key
msa = ColabFoldMSA(api_key="your-api-key")
# Or set NVCF_RUN_KEY environment variable

# Your protein sequence
sequence = "MVPSAGQLALFALGIVLAACQALENSTSPLSADPPVAAAVVSHFNDCPDSHTQFCFHGTCRFL"

# 1. Use a single database
results = msa.search_sync(
    sequence=sequence,
    databases=["Uniref30_2302"]
)

# 2. Use multiple databases
results = msa.search_sync(
    sequence=sequence,
    databases=["Uniref30_2302", "PDB70_220313"]
)

# 3. Use all databases (Cascaded Search)
results = msa.search_sync(
    sequence=sequence,
    databases=["Uniref30_2302", "PDB70_220313", "colabfold_envdb_202108"]
)

# Save results (optional)
msa.save_results(results, "results.json")
```

## Available Databases

- `Uniref30_2302`: Universal Reference Database
- `PDB70_220313`: Protein Data Bank
- `colabfold_envdb_202108`: Environmental Database

## Additional Options

```python
results = msa.search_sync(
    sequence=sequence,
    databases=["Uniref30_2302"],
    e_value=0.0001,        # E-value threshold
    iterations=1,          # Number of iterations
    output_formats=["a3m", "fasta"]  # Output formats
)
```

## Async Support

```python
import asyncio

async def search_msa():
    results = await msa.search(
        sequence=sequence,
        databases=["Uniref30_2302"]
    )
    return results

results = asyncio.run(search_msa())
```

## Development

Install with development dependencies:
```bash
pip install biomeai-nvidia[dev]
```

Run tests:
```bash
pytest
```

## License

MIT License - see the [LICENSE](LICENSE) file for details.