semvec/pypi-packages huggingface dataset:
python-https-server 1.0.1
A lightweight Python-based HTTPS static file server.
License: MIT
Requires Python: >=3.7

Simple HTTPS Server

A lightweight Python-based HTTPS file server for quick testing, CTFs, and local TLS validation.
Runs with zero dependencies and serves any directory over SSL in seconds.
Perfect for developers, pentesters, and rapid prototyping workflows.

How to Run:
python -m simplehttps.server --cert <location>/cert.pem --key <location>/key.pem
wme 0.1.6
python library for accessing the Wikimedia Enterprise API
License: MIT
Requires Python: <4.0,>=3.11
Keywords: wikimedia, api, python

# Python Wikimedia Enterprise API

This is not the official API for the Wikimedia Enterprise API.

Currently only supports the on-demand API

```python
import wme
creds = await wme.auth.login(username, password)
on_demand = wme.on_demand.OnDemand(creds)
results = await on_demand.lookup("Wikimedia Foundation")
```

## Testing

For testing make a `.env` file with `WME_USERNAME` and `WME_PASSWORD`. Run `poetry run pytest` to run tests

Install the pre-commit hooks with `poetry run pre-commit install` or just run them manually e.g. `poetry run ruff check`
aparavi-mcp 0.1.0
Aparavi MCP stdio server that streams local files to Aparavi EaaS
License: MIT
Requires Python: >=3.10

# Aparavi MCP Server

A Python [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) server that integrates with the Aparavi Platform. It exposes Aparavi data processing tools to LLMs, allowing them to stream local files directly to Aparavi EaaS for advanced processing (OCR, classification, PII detection, etc.).

## Features

- Full DTC capabilities accessible within your MCP client
- Custom pipeline tools created from your DTC workflows
- Built-in tools for immediate use (no DTC configuration required)
- Document processing and text extraction
- OCR capabilities for images and system diagrams
- Async processing support
- MCP-compliant interface

## Installation

### Option 1: Using pipx (Recommended)

Isolate the server environment:

```bash
pipx install aparavi-mcp
```

### Option 2: Using pip

Install into your current environment:

```bash
pip install aparavi-mcp
```

## Configuration

Add the server to your MCP host configuration (e.g., `mcp.json` for Cursor, `claude_desktop_config.json` for Claude Desktop).

You will need your Aparavi API key and URI.

```json
{
  "mcpServers": {
    "aparavi": {
      "command": "aparavi-mcp",
      "args": [],
      "env": {
        "APARAVI_URI": "https://eaas.aparavi.com",
        "APARAVI_AUTH": "your-api-key-here"
      }
    }
  }
}
```
> **Note:** For EU users, please use `https://eaas.aparavi.eu` for `APARAVI_URI`.

## Getting Your API Key

To obtain your API key for authentication, visit your Aparavi account settings or contact your Aparavi administrator.

## Usage

Once configured, the following tools will be available to your LLM:

### 1. Aparavi Document Processor
A convenience tool that processes a file using a standard Aparavi document processing pipeline.
- **Input**: `filepath` (Absolute path to the local file)
- **Output**: Extracted text and metadata

### 2. Custom Pipelines
Any running tasks/pipelines in your Aparavi account will also be dynamically exposed as tools.

## Pipeline Requirements

To use custom pipelines as tools, they must follow these configuration rules in DTC:

### Webhook Start

Your pipeline must start with a **webhook source node**.

### HTTP Response End (for client output)

If you need output returned to your MCP client, your pipeline must end with an **HTTP response node**.

### HTTP Response Exception

Pipeline output to your client may not always be necessary. For example, a pipeline that sends data to a **vector store node** won't require an HTTP response node, as it stores data rather than returns it to the client.

### Example Pipeline Flow

**For client output:**

`Webhook → [Your Processing Nodes] → HTTP Response`

**For vector storage:**

`Webhook → [Your Processing Nodes] → Vector Store`

Once your pipeline is configured in DTC with a webhook source, make sure to **start the pipeline**. It will automatically become available as a tool in your MCP client.