semvec/pypi-packages huggingface dataset:
arcade-tdk 3.6.0
Arcade TDK - Toolkit Development Kit for building Arcade tools
License: MIT
Requires Python: >=3.10

# Arcade TDK (Toolkit Development Kit)

Toolkit Development Kit for building and testing Arcade tools.

## Overview

Arcade TDK provides the essential tools and utilities for building Arcade tools:

- **Tool Decorator**: Simple `@tool` decorator for creating Arcade tools
- **Authentication**: Auth providers and helpers for tool security
- **Annotations**: Type annotations and parameter validation
- **Core Integration**: Seamless integration with arcade-core components

## Installation

```bash
pip install arcade-tdk
```

## Usage

```python
from typing import Annotated

from arcade_tdk import tool

@tool
def hello_world(name: Annotated[str, "The name of the person to greet"]) -> str:
    """Say hello to someone."""
    return f"Hello, {name}!"

# The tool is automatically registered and available for use
```

## Advanced Usage

```python
from typing import Annotated

from arcade_tdk import tool, ToolCatalog, Toolkit
from arcade_tdk.auth import Reddit

# Create tools with auth requirement
@tool(requires_auth=Reddit(scopes=["read"]))
def get_posts_in_subreddit(
    subreddit: Annotated[str, "The name of the subreddit"],
    limit: Annotated[int, "The number of posts to return]
) -> dict:
    """Get posts from a specific subreddit"""
    # TODO: Implement your Reddit tool
    return {}
```

## License

MIT License - see LICENSE file for details.
arcade-core 4.4.0
Arcade Core - Core library for Arcade platform
License: MIT
Requires Python: >=3.10

# Arcade Core

Core library for the Arcade platform providing foundational components and utilities.

## Overview

Arcade Core provides the essential building blocks for the Arcade platform:

- **Tool Catalog & Toolkit Management**: Core classes for managing and organizing tools
- **Configuration & Schema Handling**: Configuration management and validation
- **Authentication & Authorization**: Auth providers and security utilities
- **Error Handling**: Comprehensive error types and handling
- **Telemetry & Observability**: Monitoring and tracing capabilities
- **Utilities**: Common helper functions and validators

## Installation

```bash
pip install arcade-core
```

## Usage

1. Install an arcade toolkit
```bash
pip install arcade-math
```

2. Load the toolkit
```python
import arcade_math
from arcade_core import ToolCatalog, Toolkit

# Create a tool catalog
catalog = ToolCatalog()

# Load a toolkit
toolkit = Toolkit.from_module(arcade_math)
```

## License

MIT License - see LICENSE file for details.
arcade-mcp-server 1.17.0
Model Context Protocol (MCP) server framework for Arcade.dev
License: MIT
Requires Python: >=3.10

# Arcade MCP Server

<p align="center">
  <img src="https://docs.arcade.dev/images/logo/arcade-logo.png" alt="Arcade Logo" width="200"/>
</p>

Arcade MCP (Model Context Protocol) Server enables AI assistants and development tools to interact with your Arcade tools through a standardized protocol. Build, deploy, and integrate MCP servers seamlessly across different AI platforms.

## Quick Links

- **[Quickstart Guide](getting-started/quickstart.md)** - Get up and running in minutes
- **[Walkthrough](examples/README.md)** - Learn by example
- **[API Reference](api/app.md)** - MCPApp API documentation

## Features

- 🚀 **FastAPI-like Interface** - Simple, intuitive API with `MCPApp`
- 🔧 **Tool Discovery** - Automatic discovery of tools in your project
- 🔌 **Multiple Transports** - Support for stdio and HTTP/SSE
- 🤖 **Multi-Client Support** - Works with Claude, Cursor, and more
- 📦 **Package Integration** - Load installed Arcade packages
- 🔐 **Built-in Security** - Environment-based configuration and secrets
- 🔄 **Hot Reload** - Development mode with automatic reloading
- 📊 **Production Ready** - Deploy with Docker, systemd, PM2, or cloud platforms

## Getting Started

### Installation

```bash
pip install arcade-mcp-server
```

### Create Your First Server

```python
from arcade_mcp_server import MCPApp
from typing import Annotated

app = MCPApp(name="my-tools", version="1.0.0")

@app.tool
def greet(name: Annotated[str, "Name to greet"]) -> str:
    """Greet someone by name."""
    return f"Hello, {name}!"

if __name__ == "__main__":
    app.run()
```

### Run Your Server

```bash
# For development
python my_tools.py

# For Claude Desktop
python -m arcade_mcp_server stdio

# For HTTP clients
python -m arcade_mcp_server --host 0.0.0.0 --port 8080
```

## Community

- [GitHub Repository](https://github.com/ArcadeAI/arcade-mcp)
- [Discord Community](https://discord.gg/arcade-mcp)
- [Documentation](https://docs.arcade.dev)

## Analytics & Privacy

*Arcade MCP Server* collects anonymous usage data to help us improve the service and debug issues. We track "MCP server start" events to understand server usage patterns and reliability.

#### What We Track

When the server starts, we collect the following information:
- **Server configuration**: transport type (`http` or `stdio`), host, port
- **Server metadata**: tool count, server version
- **Runtime environment**: Python version, OS type and release
- **Timing**: device timestamp
- **Errors**: error messages (if startup fails)

#### Privacy

- For **anonymous users**: Events are tracked with an anonymous ID and no user profile is created
- For **authenticated users**: Events are linked to your account to help us provide better support
- **No sensitive data** (credentials, tool inputs/outputs, or personal information) is ever collected

#### Opt Out

To disable usage tracking, set the environment variable ARCADE_USAGE_TRACKING to 0.

## License

Arcade MCP Server is open source software licensed under the MIT license.
redivis 0.19.5
Redivis python client library
License: MIT
Requires Python: >=3.6.0


![Redivis Logo](https://github.com/redivis/redivis-python/raw/main/assets/logo_small.png)

# redivis-python

[Redivis](https://redivis.com) client library for python!

Connect data on Redivis to the Python scientific stack, upload new data to Redivis, and script your data management
tasks!

## Getting started

The easiest way to get started is
to [create a new python notebook on Redivis](https://docs.redivis.com/reference/workflows/notebooks/python-notebooks).
This package, alongside other common data science python packages, are all preinstalled.

You can also install the latest version of this package in any other python environment:

```shell
pip install redivis
```

And then you're ready to go!

```py
import redivis

iris_table = redivis.organization("Demo").dataset("Iris species").table("Iris")
df = iris_table.to_pandas_dataframe()
```

## Documentation

**[View the comprehensive documentation here >](https://apidocs.redivis.com/client-libraries/redivis-python)**

This package contains a number of methods for processing data on Redivis and interfacing with the API through python.
Consult
the documentation for a detailed list of all methods and accompanying examples.

## Issue reporting

Please report any issues or feature requests in the [issue tracker](https://github.com/redivis/redivis-python/issues).

## Contributing

Please report any issues or feature requests in the [issue tracker](https://github.com/redivis/redivis-python/issues)
— your feedback is much appreciated!