semvec/pypi-packages huggingface dataset:
Apptimize 1.2.44
Apptimize Python Server SDK
Requires Python: >=3.5.1
Keywords: apptimize testing ab experimentation optimize

===========================
Apptimize Python Server SDK
===========================

Description
===========

The Apptimize Python Server SDK allows application developers to run A/B tests and manage feature flags across Python server instances. The SDK is used by Apptimize Cross-Platform customers in conjunction with the Apptimize Dashboard, which provides a web-based interface for managing experiments and feature flags.

In addition, the Apptimize Python Server SDK can be used with client-side SDKs from Apptimize for iOS, Android, tvOS, Android TV, and JavaScript in order to manage the entire end-user digital experience and understand how users interact across different channels.

For more information or to start a free trial of Apptimize’s Cross-Platform experimentation product, please visit https://apptimize.com



Documentation
=============

Documentation for Apptimize's product and the Python Server SDK can be found at https://apptimize.com/docs/
mcp-server-testing 0.2.0
A test server implementation for Model Context Protocol (MCP)
Requires Python: >=3.10
Keywords: automation, llm, mcp, model-context-protocol

# MCP Test Server

This project is a test server implementation for the Model Context Protocol (MCP). It uses the FastMCP framework to provide basic MCP server functionality.

## Available Tools

### greet

A function that receives a name and returns a greeting message.

- `name` (string, required): The name to greet
  - **Returns**: A string containing the greeting message

## Installation

### Using `uv` (Recommended)

No special installation is required when using `uv`. You can run `mcp-server-testing` directly using `uvx`.

### Using PIP

Alternatively, you can install `mcp-server-testing` using pip:

```sh
pip install mcp-server-testing
```

After installation, you can run the script as follows:

```sh
mcp-server-testing
```

## Configuration

### Configure for Claude.app

Add to your Claude settings:

<details>
<summary>Using uvx</summary>

```json
{
  "mcpServers": {
    "testing": {
      "command": "uvx",
      "args": ["mcp-server-testing"]
    }
  }
}
```

</details>

### Configure for VS Code

For quick installation, use one of the one-click install buttons below...

[![Install with UV in VS Code](https://img.shields.io/badge/VS_Code-UV-0098FF?style=flat-square&logo=visualstudiocode&logoColor=white)](https://insiders.vscode.dev/redirect/mcp/install?name=testing&config=%7B%22command%22%3A%22uvx%22%2C%22args%22%3A%5B%22testing%22%5D%7D)

### Command Line Options

You can specify the following options when running the server:

- `--sse`: Enable SSE transport

  - **Choices**: `on`, `off`
  - **Default**: `off`
  - **Description**: Enables SSE transport when set to "on"

- `--host`: Host to bind the server

  - **Default**: `localhost`
  - **Description**: Specifies the host address the server should bind to

- `--port`: Port to bind the server

  - **Type**: Integer
  - **Default**: `8000`
  - **Description**: Specifies the port number for the server to bind to

- `--log-level`: Set the logging level

  - **Choices**: `debug`, `info`, `warning`, `error`
  - **Default**: `info`
  - **Description**:

    - `debug`: Detailed debugging information
    - `info`: General runtime information (default)
    - `warning`: Potential issues that do not affect execution
    - `error`: Errors that occur during execution

## Development

This project serves as a basic implementation example of an MCP server. To add new features, implement them in `server.py`.

During development, you can test your scripts with the following commands:

```sh
uv pip install -e .
mcp-server-testing
```
logdash 1.1.0
Python SDK for logdash logging and metrics service
Requires Python: >=3.7
Keywords: logging, metrics, monitoring

# logdash - Python SDK

Logdash is a zero-config observability platform. This package serves as a Python interface to use it.

## Pre-requisites

Setup your free project in less than 2 minutes at [logdash.io](https://logdash.io/)

## Installation

```bash
pip install logdash
```

## Logging

```python
from logdash import create_logdash

# Initialize with your API key
logdash = create_logdash({
    # optional, but recommended to see your logs in the dashboard
    "api_key": "<your-api-key>",
})

# Access the logger
logger = logdash.logger

logger.info("Application started successfully")
logger.error("An unexpected error occurred")
logger.warn("Low disk space warning")
```

## Metrics

```python
from logdash import create_logdash

# Initialize with your API key
logdash = create_logdash({
    # optional, but recommended as metrics are only hosted remotely
    "api_key": "<your-api-key>",
})

# Access metrics
metrics = logdash.metrics

# to set absolute value
metrics.set("users", 0)

# to modify existing metric
metrics.mutate("users", 1)
```

## View

To see the logs or metrics, go to your project dashboard

![logs](docs/logs.png)
![delta](docs/delta.png)

## License

This project is licensed under the MIT License.

## Contributing

Contributions are welcome! Feel free to open issues or submit pull requests.

## Support

If you encounter any issues, please open an issue on GitHub or let us know at [contact@logdash.io](mailto:contact@logdash.io).