semvec/pypi-packages huggingface dataset:
iflow-mcp_mitsuhiko-pexpect-mcp 0.1.0
MCP server for remote pexpect session control
Requires Python: >=3.12.1

# pexpect-mcp

An [MCP](https://modelcontextprotocol.io/) (Model Context Protocol) server that
provides remote pexpect session control for debugging and process interaction.

## Overview

This MCP server enables AI assistants to execute Python code with pexpect
functionality, allowing for interactive debugging sessions with tools like
LLDB, GDB, and other command-line utilities that require programmatic
interaction.

In some sense this is less of a pexpect MCP as one that is just maintaining
a stateful Python session.

## Installation

```bash
uv tool install git+https://github.com/mitsuhiko/pexpect-mcp
```

## Usage

### As an MCP Server

Add to your Claude Code configuration:

```json
{
  "mcpServers": {
    "pexpect": {
      "command": "pexpect-mcp"
    }
  }
}
```

### Tool Usage

The server provides a single tool: `pexpect_tool`

**Parameters:**
- `code` (string): Python code to execute with pexpect support
- `timeout` (optional int): Timeout in seconds (default: 30)

**Example Usage:**

```python
# Start a debugging session
child = pexpect.spawn('lldb ./my-program')
child.expect('(lldb)')

# Run the program
child.sendline('run')
child.expect('(lldb)')
print(child.before.decode())

# Get backtrace
child.sendline('bt')
child.expect('(lldb)')
print(child.before.decode())
```

## Demo

The repository includes a demo with a buggy C program (`demo-buggy.c`) that can
be debugged using LLDB through the pexpect interface. This demonstrates the
server's capability for interactive debugging sessions.

```
The program `./demo-buggy` crashes when executed. Use LLDB to:

- Start the program under the debugger
- Identify where and why it crashes
- Examine variables, memory, and call stack
- Report the root cause of the crash
```

## Requirements

- Python ≥ 3.12.1
- pexpect ≥ 4.9.0
- mcp ≥ 1.13.0

## License

See LICENSE file for details.
digestify 0.1.3
Digestify is a Python library that generates comprehensive news digests and content summaries for specific topics using OpenAI and the WebQuest MCP server.
Requires Python: >=3.12

# Digestify

Digestify is a Python library that generates comprehensive news digests and content summaries for specific topics using OpenAI and the WebQuest MCP server. It intelligently gathers latest updates, including processing video transcripts, to provide structured summaries.

## Installation

Installing using pip:

```bash
pip install digestify
```

Installing using uv:

```bash
uv add digestify
```

## Configuration

Digestify requires the following environment variables to be set (either in your environment or a `.env` file):

- `OPENAI_API_KEY`: Your OpenAI API key.
- `WEBQUEST_MCP_ACCESS_TOKEN`: Access token for the WebQuest MCP server.
- `WEBQUEST_MCP_URL`: URL for the WebQuest MCP server.
- `OPENAI_MODEL`: (Optional) The OpenAI model to use (default: "gpt-5.2").
- `MAX_TOOL_CALLS`: (Optional) Maximum number of tool calls allowed (default: 5).
- `MAX_OUTPUT_TOKENS`: (Optional) Maximum number of tokens for the output (default: 100000).

## Usage

Here is a simple example of how to use Digestify to get a digest for a topic:

```python
import asyncio
from digestify import Digestify, Topic

async def main():
    # Define the topic you want a digest for
    topic = Topic(
        name="Artificial Intelligence",
        description="Latest advancements in AI, machine learning, and LLMs."
    )

    # Initialize the client (automatically loads settings from env/files)
    client = Digestify()

    # Get the digest
    digest = await client.get_stories(topic)

    # Print stories
    for story in digest.stories:
        print(f"Title: {story.title}")
        print(f"Published: {story.published_at}")
        print(f"Summary: {story.content}")
        print("References:")
        for url in story.reference_urls:
            print(f"- {url}")
        print("-" * 20)

if __name__ == "__main__":
    asyncio.run(main())
```