semvec/pypi-packages huggingface dataset:
excel-to-markdown-mcp 0.1.0
Excel转Markdown MCP服务器 - Python版本
Requires Python: >=3.11

# Excel转Markdown

这是一个基于Python的MCP (Model Context Protocol) 服务器，用于将Excel文件转换为Markdown格式。

## 功能特性

- 🚀 直接使用Python的`markitdown`库，支持Excel处理
- 📊 支持多种Excel格式 (.xlsx, .xls)
- 🗂️ 支持多工作表转换
- 🔧 简单的API接口
- 🛡️ 完整的错误处理

## 安装和使用

### 1. 安装依赖

```bash
cd /Users/fengjinchao/Desktop/mcp/skills/python/excel-to-markdown
uv sync
```

### 2. Claude配置

在Claude的MCP配置中添加：

```json
{
  "excel-to-markdown-python": {
    "name": "Excel转markdown(Python)",
    "type": "stdio",
    "description": "Python版本的Excel转markdown工具，支持多工作表",
    "isActive": true,
    "command": "uv",
    "args": ["--directory", "/Users/fengjinchao/Desktop/mcp/skills/python/excel-to-markdown", "run", "excel-to-markdown-mcp"]
  }
}
```

### 3. 使用工具

#### 基本用法
```json
{
  "name": "xlsx-to-markdown",
  "arguments": {
    "filepath": "/path/to/your/spreadsheet.xlsx"
  }
}
```

#### 指定工作表
```json
{
  "name": "xlsx-to-markdown", 
  "arguments": {
    "filepath": "/path/to/your/spreadsheet.xlsx",
    "sheet_name": "Sheet1"
  }
}
```

## 支持的文件格式

- `.xlsx` - Excel 2007+ 格式
- `.xls` - Excel 97-2003 格式

## Python版本的优势

1. **更强大的Excel支持**: Python生态系统有更好的Excel处理库
2. **更好的性能**: 避免了子进程调用的开销
3. **更简洁的代码**: 直接API调用，无需复杂的进程管理
4. **更好的错误处理**: Python异常处理机制更完善
5. **扩展性更强**: 易于添加新功能（如工作表选择、格式选项等）

## 代码结构

```
excel-to-markdown/
├── pyproject.toml               # 项目配置
├── README.md                   # 说明文档
└── excel_to_markdown_mcp/      # 主要代码
    ├── __init__.py            # 包初始化
    └── server.py              # MCP服务器实现
```

## vs TypeScript 版本

| 特性 | Python版本 | TypeScript版本 |
|------|-----------|---------------|
| 代码行数 | ~120行 | ~150+行 |
| 依赖安装 | `uv sync` | `npm install + uv sync` |
| 构建步骤 | 无需构建 | `npm run build` |
| Excel支持 | 原生支持 | 通过子进程 |
| 错误处理 | Python异常 | 复杂的进程错误处理 |
| 维护性 | 更简单 | 更复杂 |
jacoco-reporter 0.1.0
A MCP server project
Requires Python: >=3.13

# jacoco-reporter MCP server

A MCP server project

## Components

### Resources

The server provides access to JaCoCo coverage reports through custom URI schemes.

### Prompts

The server provides a single prompt:
- jacoco-report-analysis: Analyzes JaCoCo coverage reports
  - Optional "style" argument to control detail level (brief/detailed)

### Tools

The server implements one tool:
- jacoco-reporter-server: Reads JaCoCo reports and returns coverage data
  - Takes "jacoco_xmlreport_absolute_path" as required argument
  - Optional "covered_types" and "clazz" arguments

## 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": {
    "jacoco-reporter": {
      "command": "uv",
      "args": [
        "--directory",
        "/Users/chaney/PycharmProjects/mcp/jacoco-reporter",
        "run",
        "jacoco-reporter"
      ]
    }
  }
  ```
</details>

<details>
  <summary>Published Servers Configuration</summary>
  ```
  "mcpServers": {
    "jacoco-reporter": {
      "command": "uvx",
      "args": [
        "jacoco-reporter"
      ]
    }
  }
  ```
</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 /Users/chaney/PycharmProjects/mcp-jacoco-reporter/jacoco-reporter run jacoco-reporter
```


Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.
qernel 0.0.1.dev0
An agentic, virtual kernel for efficient programming of quantum systems
Requires Python: >=3.9

### Creating Your Algorithm

1. **Create your algorithm file** (e.g., `my_algorithm.py`):
```python
import cirq
from qernel import Algorithm

class MyAlgorithm(Algorithm):
    def get_name(self) -> str:
        return "My Algorithm"
    
    def get_type(self) -> str:
        return "my_algorithm"
    
    def build_circuit(self, params: dict) -> cirq.Circuit:
        # Your quantum circuit implementation here
        q = cirq.LineQubit.range(2)
        circuit = cirq.Circuit(
            cirq.H(q[0]),
            cirq.CNOT(q[0], q[1])
        )
        return circuit

# Create an instance for testing
algorithm = MyAlgorithm()
```

2. **Create your specification file** (`spec.yaml`):
```yaml
algorithm:
  name: "my_algorithm"
  type: "my_algorithm"
  epsilon: 0.01
  payoff: "max"
  hardware_preset: "GF-realistic"
```

### Using Visualization

Qernel includes a real-time visualization system that provides a live window into quantum algorithm execution. To use it:

```python
from qernel import QernelClient

# Create a standard client
client = QernelClient("https://api.qernel.example.com")

# Run with real-time visualization
result = client.run_algorithm_with_visualization("my_algorithm.py", "spec.yaml")
```
HakObserverLinuxpy 2.2.3
A package connect endpoints to the Hakware Application for Linux
Requires Python: >=3.6

# Hakware-py

HakObserver Linux is a Python package that allows endpoints to connect to the Hakware solution

## Installation

To install HakObserverLinuxpy, ensure you have Python installed (version 3.6 or higher), then use pip to install the package:

```bash
pip install HakObserverLinuxpy
jbssdk 0.0.8
A small example package
Requires Python: >=3.10

# Cathay JBS Python SDK

跨项目使用的JBS Python SDK

# 安装

> 版本要求 `python>=3.10`

```bash
pip install jbssdk
```

# 使用

```python
from jbssdk.Client import JBS
import asyncio


async def main():
    jbs = JBS()
    res = await jbs.get_deal_after_info()
```
openfeature-provider-flagsmith 0.1.5
Openfeature provider for Flagsmith
Requires Python: <4.0,>=3.9

# flagsmith-openfeature-provider-python

The Flagsmith provider allows you to connect to your Flagsmith instance through the OpenFeature SDK

## Python SDK usage

### Install dependencies

First, you'll need to install the OpenFeature SDK and the Flagsmith Provider.  

```bash
pip install openfeature-sdk openfeature-provider-flagsmith
```

### Using the Flagsmith Provider with the OpenFeature SDK

To create a Flagsmith provider you will need to provide a number of arguments. These are shown and described 
below. See the [Flagsmith docs](https://docs.flagsmith.com/clients/server-side) for further information on the 
configuration options available for the Flagsmith python client.

```python
from flagsmith import Flagsmith
from openfeature_flagsmith.provider import FlagsmithProvider

provider = FlagsmithProvider(
    # Provide an instance of the Flagsmith python client.
    # Required: True
    client=Flagsmith(...),
    
    # By enabling the use_flagsmith_defaults setting, you can instruct the OpenFeature SDK to use
    # the default logic included in the Flagsmith client as per the docs here: 
    # https://docs.flagsmith.com/clients/server-side#managing-default-flags. This will override the 
    # default provided at evaluation time in the OpenFeature SDK in most cases (excluding those where 
    # an unexpected exception happens in the Flagsmith client itself).
    # Required: False
    # Default: False
    use_flagsmith_defaults=False,
    
    # By default, when evaluating the boolean value of a feature in the OpenFeature SDK, the Flagsmith 
    # OpenFeature Provider will use the 'Enabled' state of the feature as defined in Flagsmith. This 
    # behaviour can be changed to use the 'value' field defined in the Flagsmith feature instead by 
    # enabling the use_boolean_config_value setting. 
    # Note: this relies on the value being defined as a Boolean in Flagsmith. If the value is not a 
    # Boolean, an error will occur and the default value provided as part of the evaluation will be 
    # returned instead.  
    # Required: False
    # Default: False
    use_boolean_config_value=False,
    
    # By default, the Flagsmith OpenFeature Provider will raise an exception (triggering the 
    # OpenFeature SDK to return the provided default value) if the flag is disabled. This behaviour
    # can be configured by enabling this flag so that the Flagsmith OpenFeature provider ignores
    # the enabled state of a flag when returning a value.
    # Required: False
    # Default: False
    return_value_for_disabled_flags=False,
)
```

The provider can then be used with the OpenFeature client as per 
[the documentation](https://openfeature.dev/docs/reference/concepts/evaluation-api#setting-a-provider).