semvec/pypi-packages huggingface dataset:
ThiagoCalc 0.2.0
Uma biblioteca incrível!
Requires Python: >=3.6

DocumentaÃ§Ã£o da Calculadora
Este mÃ³dulo contÃ©m uma implementaÃ§Ã£o simples de uma calculadora com operaÃ§Ãµes bÃ¡sicas (soma, subtraÃ§Ã£o, multiplicaÃ§Ã£o e divisÃ£o). O mÃ³dulo tambÃ©m inclui um mecanismo de interaÃ§Ã£o com o usuÃ¡rio para executar essas operaÃ§Ãµes de forma interativa no terminal.

Estrutura do CÃ³digo
O cÃ³digo Ã© composto por duas classes principais:

Calculadora: Realiza as operaÃ§Ãµes matemÃ¡ticas.

User: ResponsÃ¡vel pela interaÃ§Ã£o com o usuÃ¡rio e gerenciamento do fluxo de operaÃ§Ãµes.

1. Classe Calculadora
A classe Calculadora Ã© responsÃ¡vel pelas operaÃ§Ãµes aritmÃ©ticas bÃ¡sicas. Ela tem o seguinte formato:

somar(): Retorna a soma de numero1 e numero2.
subtrair(): Retorna a subtraÃ§Ã£o de numero1 e numero2.
multiplicar(): Retorna a multiplicaÃ§Ã£o de numero1 e numero2.
dividir(): Retorna a divisÃ£o de numero1 por numero2. Caso numero2 seja zero, retorna uma mensagem de erro ("Erro: divisÃ£o por zero nÃ£o Ã© permitida").
Exemplo de uso da Calculadora:
python
Copiar cÃ³digo
calc = Calculadora(10, 5)
print(calc.somar())  # Output: 15
print(calc.subtrair())  # Output: 5
print(calc.multiplicar())  # Output: 50
print(calc.dividir())  # Output: 2.0


pip install
algoai 0.2.5
A collection of AI algorithms
Requires Python: >=3.6


# algoai

`algoai` is a Python package designed to implement and demonstrate various Artificial Intelligence algorithms. It includes solutions for search problems, optimization, game theory, and constraint satisfaction, among others. This package can be useful for students, enthusiasts, and developers interested in learning and experimenting with AI algorithms.



## Installation

To install `algoai`, you can use `pip`:

```bash
pip install algoai
```
pyaskalono 0.1.3
Python bindings for askalono - rust library to detect license texts
Requires Python: >=3.7

# pyaskalono

Python bindings for [askalono](https://github.com/jpeddicord/askalono) - rust library to detect license texts

## Installation

```
pip install pyaskalono
```

## Use

```python
from askalono import identify

LICENSE = '''
The MIT License (MIT)

Copyright © 2021 <copyright holders>

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
'''

detected_license = identify(LICENSE)

detected_license.name # 'MIT'
detected_license.score # 0.9878048896789551
```

## Publishing

To upload new package run `maturin publish` from pipenv environment
crawl4ai-mcp-service 0.1.0
MCP server for Crawl4AI integration
Requires Python: >=3.13

# crawl4ai_mcp_service MCP server

crawl4ai_mcp_service

## 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": {
    "crawl4ai_mcp_service": {
      "command": "uv",
      "args": [
        "--directory",
        "/Users/unclecode/devs/crawl4ai_mcp_service",
        "run",
        "crawl4ai_mcp_service"
      ]
    }
  }
  ```
</details>

<details>
  <summary>Published Servers Configuration</summary>
  ```
  "mcpServers": {
    "crawl4ai_mcp_service": {
      "command": "uvx",
      "args": [
        "crawl4ai_mcp_service"
      ]
    }
  }
  ```
</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/unclecode/devs/crawl4ai_mcp_service run crawl4ai-mcp-service
```


Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.