semvec/pypi-packages huggingface dataset:
stdio-python-demo 0.1.3
A simple MCP server implementation using stdio protocol
Requires Python: >=3.11

# MCP Python Stdio 示例项目

这是一个可分发的 Model Context Protocol (MCP) 服务端示例，采用了 `stdio` 协议进行通讯。

## 目录结构
项目采用了标准推荐的 `src` 布局：
- `src/stdio_python_demo/`: 源代码目录
- `pyproject.toml`: 包含打包元数据和 entry points 配置

## 如何作为用户直接运行

如果你将此项目上传到 GitHub，其他用户**无需手动下载代码**，可以直接通过 `uvx` 运行：

```bash
# 替换为你的 GitHub 仓库地址
uvx --from git+https://github.com/YOUR_USERNAME/stdio-python-demo.git stdio-python-demo
```

## 本地开发与运行

### 1. 安装环境
```bash
uv sync
```

### 2. 运行服务端
项目配置了 `stdio-python-demo` 入口点，可以直接运行：
```bash
uv run stdio-python-demo
```

### 3. 测试验证
运行模拟客户端脚本：
```bash
uv run test_client.py
```

或者使用 MCP Inspector：
```bash
npx @modelcontextprotocol/inspector uv run stdio-python-demo
```

## 分发到公共仓库 (PyPI) 的建议

如果你想让用户直接通过 `uvx stdio-python-demo` 运行：
1. 注册 [PyPI](https://pypi.org/) 账号。
2. 在项目根目录运行 `uv build`。
3. 使用 `uv publish` 将构建好的包发布。

发布后，用户的运行体验将非常顺滑：
```bash
uvx stdio-python-demo
```

```json
{
  "mcpServers": {
    "python-calc": {
      "command": "uvx",
      "args": [
        "stdio-python-demo"
      ],
      "env": {
        "GREETING_PREFIX": "你好"
      }
    }
  }
}
```

```json
{
  "mcpServers": {
    "python-calc": {
      "command": "uv",
      "args": [
        "--directory",
        "D:/payegis/mcp/base-demo/stdio-python-demo",
        "run",
        "stdio-python-demo"
      ],
      "env": {
        "GREETING_PREFIX": "你好"
      }
    }
  }
}
```

## 环境变量配置示例

本项目演示了两种配置环境变量的方式：

### 1. 使用 `.env` 文件（本地开发推荐）
在项目根目录下创建 `.env` 文件，你可以配置自定义的问候语前缀：
```env
GREETING_PREFIX="你好"
```
服务端在初始化时会调用 `load_dotenv()`，你可以通过 `say_greeting` 工具来验证配置是否生效。

### 2. 在 Claude Desktop 中直接配置
如果你在 Claude Desktop 中使用此项目，可以在配置文件的 `env` 字段中注入环境变量：
```json
{
  "mcpServers": {
    "python-calc": {
      "command": "uv",
      "args": [
        "--directory",
        "D:/payegis/mcp/base-demo/stdio-python-demo",
        "run",
        "stdio-python-demo"
      ],
      "env": {
        "GREETING_PREFIX": "尊敬的"
      }
    }
  }
}
```

### 3. 可用工具演示
- `add_numbers(a, b)`: 基础数值加法。
- `say_greeting(name)`: 演示从环境变量获取前缀并拼接姓名。

### 4. 安全建议
- **不要提交 `.env`**：敏感信息（如 API Key）应仅存在于本地 `.env` 中，本项目已将其加入 `.gitignore`。
- **配置模板**：参考 `.env.example` 创建你自己的本地配置。
mosh 0.1.0
Mappable Objects, Searchable, Heuristics - A scale-to-zero data storage and search library
Requires Python: >=3.13

# Welcome to the `mosh` Pit

For many types of data, you want to be able to store it, search it. You want to apply functions to map data to other 
data. You want it to be fast, durable, and easy to use. You want a simple API. It needs to be economical and
scale to only what you use.

It needs to work well on your local computer and doesn't require extensive cloud resources like sophisticated databases.

# What is `mosh`

- M - Mappable
- O - Objects
- S - Searchable and Scales to Zero
- H - Search and storage heuristics


- P - Persistent (cloud storage)
- I - Indexed Automatically (for speed)
- T - Threaded (i.e., concurrency, parallelism, utilizes multiple cores)

`mosh` is based on objects, currently `pydantic` objects.

# Requirements

- Natural way to store a wide variety of data objects
- Searchable
- Scales to Zero
- Simple API
- Mappable (one mosh object can be mapped to another object via function)
- Fast for Local Compute (e.g., Laptops/Desktops/Workstations)
- Durable data (can synch to the cloud and/or other nodes)
- Eventually Consistent

## Non-Features

- Transactions
- Performant with Huge Datasets
deepagent-code 0.1.5
A Claude Code-style CLI for running LangGraph agents from the terminal
Requires Python: >=3.11
Keywords: langgraph, cli, agents, llm, ai, claude-code

# deepagent-code

A Claude Code-style CLI for running LangGraph agents from the terminal.

![deepagent-code](examples/image.png)

## Installation

```bash
pip install deepagent-code
```

Or install directly from GitHub:
```bash
pip install git+https://github.com/dkedar7/deepagent-code.git
```

## Quick Start

Run with the default agent (requires `ANTHROPIC_API_KEY`):
```bash
export ANTHROPIC_API_KEY="your_api_key"
deepagent-code
```

Or specify your own agent:
```bash
deepagent-code -a path/to/your_agent.py:graph
```

This launches an interactive conversation loop with your agent.

## Usage

```bash
# Use the default agent
deepagent-code

# Send a message directly
deepagent-code "Hello, agent!"

# Specify a custom agent file
deepagent-code -a my_agent.py:graph

# Use a module path
deepagent-code -a mypackage.agents:chatbot

# Read message from a file
deepagent-code -f ./prompt.md

# Non-interactive mode (auto-approve tool calls)
deepagent-code --no-interactive

# Verbose output
deepagent-code -v
```

## Commands

In the interactive loop:
- `/q` or `/quit` - Exit
- `/c` - Clear conversation history
- `/h` or `/help` - Show help

## Environment Variables

```bash
# Agent location (path/to/file.py:variable_name or module:variable)
export DEEPAGENT_SPEC="my_agent.py:graph"
deepagent-code

# Working directory
export DEEPAGENT_WORKSPACE_ROOT="/path/to/workspace"

# Configuration
export DEEPAGENT_CONFIG='{"configurable": {"thread_id": "1"}}'
```

## CLI Options

```
Usage: deepagent-code [OPTIONS] [MESSAGE]

Arguments:
  MESSAGE  Optional input to send to the agent immediately

Options:
  -a, --agent TEXT                Agent spec (path/to/file.py:graph or module:graph)
  -g, --graph-name TEXT           Graph variable name (default: "graph")
  -f, --file PATH                 Read message from a file (any extension)
  -c, --config TEXT               Config JSON or file path
  --interactive/--no-interactive  Handle interrupts (default: interactive)
  --async-mode/--sync-mode        Async streaming (default: sync)
  -v, --verbose                   Verbose output
```

## Creating Your Own Agent

Your agent file should export a compiled LangGraph graph:

```python
# my_agent.py
from deepagents import create_deep_agent
from langgraph.checkpoint.memory import MemorySaver

agent = create_deep_agent(
    name="My Agent",
    model="anthropic:claude-sonnet-4-20250514",
    checkpointer=MemorySaver(),
)
```

Then run it:
```bash
deepagent-code -a my_agent.py:agent
```

## Programmatic Use

```python
from deepagent_code import stream_graph_updates, prepare_agent_input

input_data = prepare_agent_input(message="Hello!")

for chunk in stream_graph_updates(graph, input_data):
    if chunk.get("chunk"):
        print(chunk["chunk"], end="")
```

## License

MIT License - see LICENSE file for details.
azul-client 9.0.24
Interact with Azul using your terminal instead of clicking in the UI a thousand times!
Requires Python: >=3.12

# Azul Client

Azul client is a near complete client for Azul's RestAPI.

Interact with Azul using your terminal instead of clicking in the UI a thousand times!

Tested on ubuntu 22.04.

## Install

`pip install azul-client`

## Setup

Azul Client requires a config file located at ~/.azul.ini

A default config will be generated on first run.

You will need to adjust the config options as appropriate.

```yaml
[default]
azul_url = http://localhost
oidc_url = http://keycloak/.well-known/openid-configuration
auth_type = callback
auth_scopes =
auth_client_id = azul-web
auth_client_secret =
azul_verify_ssl = True
auth_token = {}
auth_token_time = 0
max_timeout = 300.0
oidc_timeout = 10.0
```

### Root CA

If you have extra Root CAs, you will need to make httpx aware of them or it will complain.

Ubuntu - `export SSL_CERT_FILE=/etc/ssl/certs/ca-certificates.crt`

Red Hat - `export SSL_CERT_FILE=/etc/ssl/certs/ca-bundle.crt`

Alternatively you can point to a certificate directory

`export SSL_CERT_DIR=/etc/ssl/certs`

This can be added to your ~/.bashrc to prevent you from having to do it for every terminal session.

## Usage

For usage guidance refer to the [API](./docs/api.md) and [CLI](./docs/cli.md) documentation.

## Integration test suite

The integration test suite is in the tests/integration folder.

The `setUpModule` method in the file `tests/integration/__init__.py` creates all files in azul that need to be available for querying and uploading child/dataless.
It also waits for those uploaded files to be available in Azul which means during tests you can assume those files exist.

It also exports the sha256's of the files it uploaded to ensure the tests can import those sha256's for their testing.

NOTE - the first time you run the test suite particularly if you've added new files to the module it may be slow. But all subsequent runs will be much faster.
pftcli 1.0.0.dev1
CLI acessível para automação de trading
Requires Python: >=3.13
Keywords: trading, cli, automation, market, broker

# pftcli

CLI acessível para automação de trading via integração com uma DLL de plataforma de mercado.

O **pftcli** permite interagir com uma DLL de trading instalada localmente, facilitando automação, testes, inspeção de estado e integração com outros sistemas via linha de comando.

> Projeto focado em simplicidade, acessibilidade em terminal e uso técnico por desenvolvedores.

---

## Características

- Interface de linha de comando (CLI)
- Saída textual amigável para leitores de tela (NVDA, JAWS, etc.)
- Foco em automação e integração
- Não utiliza gráficos ou interfaces visuais
- Projetado para uso em **Windows**

---

## 📦 Instalação

Via PyPI:

```bash
pip install pftcli
````

Ou usando Poetry:

```bash
poetry add pftcli
```

---

## Uso básico

Após a instalação, os seguintes comandos ficam disponíveis:

```bash
pft --help
```

ou

```bash
pftcli --help
```

Exemplo:

```bash
pft status
```

*(Os comandos disponíveis dependem da implementação atual do CLI.)*

---

## Requisitos

* Python **3.13 ou superior**
* Sistema operacional **Windows**
* DLL da plataforma de trading devidamente instalada e licenciada no ambiente do usuário

---

## Aviso importante (Disclaimer)

Este projeto:

* **Não é afiliado, patrocinado ou endossado por qualquer empresa proprietária da plataforma ou da DLL utilizada**
* **Não distribui DLLs proprietárias**
* Depende de uma DLL instalada separadamente pelo usuário, sob licença válida

O usuário é inteiramente responsável por cumprir os termos de licença da plataforma e da DLL utilizada.

---

## Licença

Este projeto é distribuído sob a licença **GNU General Public License v3.0 ou posterior (GPL-3.0-or-later)**.

Você pode:

* Usar
* Estudar
* Modificar
* Redistribuir

Desde que qualquer redistribuição de versões modificadas também seja feita sob a GPL.

Consulte o arquivo [`LICENSE`](LICENSE) para mais detalhes.

---

## Desenvolvimento

Clone o repositório:

```bash
git clone https://github.com/seu-usuario/pftcli.git
cd pftcli
```

Instale dependências:

```bash
poetry install
```

Execute localmente:

```bash
poetry run pft --help
```

---

## Contribuições

Contribuições são bem-vindas.

* Abra issues para bugs ou sugestões
* Envie pull requests com descrições claras
* Priorize legibilidade, simplicidade e acessibilidade em terminal

---

## Status do projeto

🚧 **Em desenvolvimento (Alpha)**
A API e os comandos podem mudar sem aviso prévio.
calx-cli 0.3.0
A CLI tool for displaying calendar information with terminal graphics
Requires Python: >=3.12

# calx

Calendar Expanded -- a CLI tool for displaying calendar information with terminal graphics.

```
╭───────── ISO Week ─────────╮  ╭────── February 2026 ──────╮
│                            │  │                           │
│  ╭─────────────┬────────╮  │  │    Mo Tu We Th Fr Sa  Su  │
│  │ Week        │ 6      │  │  │  5                     1  │
│  │ Year        │ 2026   │  │  │  6  2  3  4  5  6  7   8  │
│  │ Day         │ Monday │  │  │  7  9 10 11 12 13 14  15  │
│  │ Day of Year │ 33     │  │  │  8 16 17 18 19 20 21  22  │
│  ╰─────────────┴────────╯  │  │  9 23 24 25 26 27 28      │
│                            │  │                           │
╰────────────────────────────╯  ╰───────────────────────────╯
```

## Features

- ISO and simple week numbering
- Configurable first day of week (Monday through Sunday)
- Current day highlighting
- Week numbers alongside the calendar grid
- Single month, three-month, and full-year views
- Shortcut flags for common configurations (`--simple-sunday`, `--iso-sunday`, `--simple-monday`)
- Custom configuration via `calx show` with `-d` (day) and `-s`/`-i` (style) flags

## Installation

### Homebrew (recommended)

```bash
brew tap ocolunga/calx
brew install calx-cli
```

### pip

```bash
pip install calx-cli
```

### uv

```bash
uv pip install calx-cli
```

## Usage

```bash
# Default view (ISO week, Monday start)
calx

# Three-month view
calx -m

# Full-year view
calx -y

# Sunday-start with simple week numbering
calx --simple-sunday

# Custom: Wednesday start, simple numbering
calx show -d 3 -s

# Custom: Sunday start, ISO-style numbering
calx show -d 7 -i
```

## Development

```bash
uv sync --group dev
uv run calx --help

# Lint and format
uv run ruff check src/
uv run ruff format src/

# Type check
uv run ty check src/
```

## License

MIT
dj-cli-tools 0.4
dj-cli-tools is a command-line utility for Django developers designed to streamline the process of creating applications and managing boilerplate code.
Requires Python: >=3.13

# dj-cli-tools
![dj-cli-tools Banner](assets/banner.png)
**dj-cli-tools** is a command-line utility for Django developers designed to streamline the process of creating applications and managing boilerplate code. It extends Django's built-in management commands to provide enhanced scaffolding capabilities, particularly focused on Django REST Framework (DRF) workflows.

## Features

*   **Enhanced `start_app`**: Create new Django applications using custom, pre-configured templates (e.g., versioned REST API structures). **Automatically registers the new app in `INSTALLED_APPS`** in your project's `settings.py`.
*   **`create`**: A powerhouse command that generates a fully functional vertical slice for a new resource. One command creates:
    *   **Model**: Custom model definition.
    *   **Serializer**: Corresponding `ModelSerializer`.
    *   **ViewSet**: `ModelViewSet` with standard import.
    *   **Factory**: `factory_boy` factory for testing.
    *   **Admin**: Registers the model in `admin.py`.
    *   **URLs**: Registers the ViewSet in `urls.py`.

## Installation

```bash
pip install dj-cli-tools
```

Or for local development:

```bash
git clone https://github.com/AbhijithKonnayil/dj-cli-tools
cd dj-cli-tools
pip install .
```

## Usage

### 1. Starting a New App
Create a new app using a specific template. This command acts like `startapp` but better:

```bash
python manage.py start_app <app_name> --dj_template <template_name>
```

**Example:**
To create a REST API app:
```bash
python manage.py start_app core_api --dj_template simple_drf
```

**What happens?**
*   The `core_api` directory is created with the structure defined in `simple_drf`.
*   The app is automatically added to `INSTALLED_APPS` in `settings.py` (e.g., `'core_api.apps.CoreApiConfig'`).

### 2. Creating a Resource (Model, API, & More)
Generate the full stack for a new domain model in an existing app.

```bash
python manage.py create <app_name> <ModelName>
```

**Example:**
```bash
python manage.py create core_api Product
```

**What happens?**
This single command will:
1.  **Model**: Append `class Product(models.Model): ...` to `core_api/models.py`.
2.  **Serializer**: Create `ProductSerializer` in `core_api/serializers.py` with necessary imports.
3.  **Views**: Create `ProductViewSet` in `core_api/views.py`.
4.  **URLs**: Register `ProductViewSet` with a router in `core_api/urls.py` (e.g., `router.register(r'products', ProductViewSet)`).
5.  **Admin**: Register `Product` in `core_api/admin.py`.
6.  **Factories**: Create `ProductFactory` in `core_api/factories.py`.

## Requirements

*   Python 3.10+
*   Django 5.2+

## License

BSD-3-Clause