semvec/pypi-packages huggingface dataset:
chatlas 0.15.1
A simple and consistent interface for chatting with LLMs
Requires Python: >=3.9

# chatlas <a href="https://posit-dev.github.io/chatlas"><img src="https://posit-dev.github.io/chatlas/logos/hex/logo.png" align="right" height="138" alt="chatlas website" /></a>

<p>
<!-- badges start -->
<a href="https://pypi.org/project/chatlas/"><img alt="PyPI" src="https://img.shields.io/pypi/v/chatlas?logo=python&logoColor=white&color=orange"></a>
<a href="https://choosealicense.com/licenses/mit/"><img src="https://img.shields.io/badge/License-MIT-blue.svg" alt="MIT License"></a>
<a href="https://pypi.org/project/chatlas"><img src="https://img.shields.io/pypi/pyversions/chatlas.svg" alt="versions"></a>
<a href="https://github.com/posit-dev/chatlas"><img src="https://github.com/posit-dev/chatlas/actions/workflows/test.yml/badge.svg?branch=main" alt="Python Tests"></a>
<!-- badges end -->
</p>

Your friendly guide to building LLM chat apps in Python with less effort and more clarity.

## Install

Install the latest stable release [from PyPI](https://pypi.org/project/chatlas/):

```bash
pip install -U chatlas
```

Or, install the latest development version from GitHub:

```bash
pip install -U git+https://github.com/posit-dev/chatlas
```

## Quick start

Get started in 3 simple steps:

1. Choose a model provider, such as [ChatOpenAI](https://posit-dev.github.io/chatlas/reference/ChatOpenAI.html) or [ChatAnthropic](https://posit-dev.github.io/chatlas/reference/ChatAnthropic.html).
2. Visit the provider's [reference](https://posit-dev.github.io/chatlas/reference) page to get setup with necessary credentials.
3. Create the relevant `Chat` client and start chatting!

```python
from chatlas import ChatOpenAI

# Optional (but recommended) model and system_prompt
chat = ChatOpenAI(
    model="gpt-4.1-mini",
    system_prompt="You are a helpful assistant.",
)

# Optional tool registration
def get_current_weather(lat: float, lng: float):
    "Get the current weather for a given location."
    return "sunny"

chat.register_tool(get_current_weather)

# Send user prompt to the model for a response.
chat.chat("How's the weather in San Francisco?")
```


<img src="https://posit-dev.github.io/chatlas/images/chatlas-hello.png" alt="Model response output to the user query: 'How's the weather in San Francisco?'" width="67%" style="display: block; margin-left: auto; margin-right: auto">


Learn more at <https://posit-dev.github.io/chatlas>
mcp-server-coverage-report 0.1.0
Minimal MCP server boilerplate with add function
Requires Python: >=3.12

# Minimal MCP Server Boilerplate

A minimal Python MCP (Model Context Protocol) server example with a simple `add` function.

## Features

- Simple `add(a: int, b: int) -> int` function that adds two integers
- Uses `uv` for dependency management
- Runs via stdio (use with `uvx`)

## Setup

1. Install dependencies:
   ```bash
   uv sync
   ```

2. Test the server locally:
   ```bash
   uv run mcp-server-coverage-report
   ```

## Using with Cursor

To configure Cursor to use this MCP server, add the following to your Cursor MCP settings.

### Option 1: Using uvx (Recommended)

Add to your Cursor MCP configuration (usually in Cursor settings or `.cursor/mcp.json`):

```json
{
  "mcpServers": {
    "mcp-server-coverage-report": {
      "command": "uvx",
      "args": [
        "--from",
        "/mnt/c/Github/mcp-server-coverage-report",
        "mcp-server-coverage-report"
      ]
    }
  }
}
```

**Note:** Update the path `/mnt/c/Github/mcp-server-coverage-report` to match your actual project directory.

### Option 2: Using uv run

Alternatively, you can use `uv run`:

```json
{
  "mcpServers": {
    "mcp-server-coverage-report": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/mnt/c/Github/mcp-server-coverage-report",
        "mcp-server-coverage-report"
      ]
    }
  }
}
```

After adding the configuration, restart Cursor for the changes to take effect.

## Development

- Install new dependencies: `uv add <package-name>`
- Run the server: `uv run mcp-server-coverage-report`
- The server exposes one tool: `add` which takes two integers and returns their sum