semvec/pypi-packages huggingface dataset:
piazza-mcp 0.1.2
MCP server for browsing Piazza course forums
Requires Python: >=3.10
Keywords: llm, mcp, piazza

# piazza-mcp

An MCP server that lets AI agents browse your Piazza course forums — search posts, read questions/answers, and discover relevant course content.

## Install

Requires [uv](https://docs.astral.sh/uv/) (`brew install uv`).

### Claude Code

```bash
claude mcp add --scope user piazza --env PIAZZA_EMAIL=you@school.ca --env PIAZZA_PASSWORD=your-password -- uvx piazza-mcp@latest
```

### VS Code

Add to `.vscode/mcp.json`:

```json
{
  "mcp": {
    "servers": {
      "piazza": {
        "command": "uvx",
        "args": ["piazza-mcp@latest"],
        "env": {
          "PIAZZA_EMAIL": "you@school.ca",
          "PIAZZA_PASSWORD": "your-password"
        }
      }
    }
  }
}
```

Credentials are your Piazza login email and password.

## Tools

| Tool | Description |
|------|-------------|
| `list_classes()` | List active enrolled Piazza classes |
| `set_class(network_id)` | Select a class, get available folders |
| `search_posts(query, folder, limit)` | Search by keyword, folder, or both |
| `get_post(post_number)` | Read full post with answers and follow-ups |

## Development

```bash
git clone https://github.com/smchase/piazza-mcp
cd piazza-mcp
uv sync --group dev
```

Use a local clone in your MCP client:

```bash
claude mcp add piazza-dev --env PIAZZA_EMAIL=you@school.ca --env PIAZZA_PASSWORD=your-password -- uv --directory /path/to/piazza-mcp run piazza-mcp
```
iflow-mcp_arkel-ai-attio-mcp-server 0.1.1
MCP server for Attio API integration with tools to interact with Attio workspace resources
Requires Python: >=3.10

<p align=center><img src="attio.svg" alt="Attio Logo" height="48"></p>
<h1 align=center>Attio MCP Server</h1>

<p align=center>This project provides a <a href="https://gofastmcp.com">FastMCP</a> server with tools to interact with the Attio API. It allows you to manage resources within your Attio workspace.</p>

## Setup

1. **Clone the repository:**

   ```bash
   git clone <repository-url>
   cd attio-mcp
   ```
2. **Create and activate a virtual environment:**

   ```bash
   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   ```
3. **Install dependencies:**

   ```bash
   pip install -r requirements.txt
   ```
4. **Configure environment variables:**
   Create a `.env` file in the project root directory with the following content:

   ```env
   BASE_URL=https://api.attio.com
   API_KEY=your_attio_api_key_here
   ```

   Replace `your_attio_api_key_here` with your actual Attio API key.

## Running the Server

```bash
python main.py
```

The server will run on `http://0.0.0.0:8000` (or as configured in `.env`).
SSE transport is used by default available at `http://0.0.0.0:8000/sse`, you can change the transport by setting the `TRANSPORT` ('stdio', 'streamable-http' or 'sse', more info [here](https://gofastmcp.com/deployment/running-server#transport-options)).
lara-django-organisms-grpc 0.2.95
gRPC interface to lara_django_organisms
Requires Python: ==3.13.*

# lara_django_organisms_grpc

gRPC interface for lara_django_organisms - A Django-based biological organisms data management system.

## Overview

This package provides a gRPC API layer for the lara_django_organisms project, enabling efficient remote procedure calls for managing organism taxonomic data including domains, kingdoms, phyla, classes, orders, families, genera, species, and cultivars.

## Installation

```bash
pip install lara_django_organisms_grpc
```

Or with uv:

```bash
uv add lara_django_organisms_grpc
```

## Features

- High-performance gRPC interface for organism data operations
- Protocol buffer definitions for structured data exchange
- Support for CRUD operations on taxonomic entities
- Efficient streaming for bulk data operations

## Requirements

- Python 3.13
- grpcio >= 1.73.0

## Development

To install development dependencies:

```bash
uv sync --group dev
```

Run tests:

```bash
pytest
```

## Documentation

Full documentation is available at: https://larasuite.gitlab.io/lara-django-organisms/

## Source

Source code: https://gitlab.com/larasuite/lara-django-organisms

## License

See the main project for license information.
djangojasperreport 0.1.2
A simple Django wrapper for JasperReports Server REST API

# Django Jasper Report
Easily generate JasperReports from your Django views using the JasperReports Server REST API. This package handles authentication, URL construction, and maps Django responses to the correct file types automatically.

## 🛠 Prerequisites
Before using this package, you must have a JasperReports Server instance running.

You can host it yourself or use a cloud instance.

Ensure the REST API is accessible (typically at port 8080).

## 🚀 Installation
```bash
pip install djangojasperreport
```

## ⚙️ Setup
Add your JasperServer credentials to your Django `settings.py`.
```python
# settings.py
JASPER_SERVER_URL = "http://server-ip:8080"
JASPER_USER = "username"
JASPER_PASSWORD = "password"
```

## 📄 Supported Formats
Pass these strings into the `fmt` parameter to receive the corresponding file type:

Documents: `pdf`, `docx`, `rtf`, `odt`, `html`, `xml`

Spreadsheets: `xlsx`, `xls`, `csv`, `ods`

Presentations: `pptx`

## 📖 Usage
### Basic Example (DRF View)
In your `views.py`, you can extract query parameters from the URL and pass them directly to your Jasper report.

```python
from jasper_report.client import JasperClient
from rest_framework.views import APIView
from rest_framework.response import Response

class ReportView(APIView):
    def get(self, request):
        client = JasperClient()

        # 1. Path to the report unit in your JasperServer repository
        report_path = "/organization/reports/NameofReport"

        # 2. Get all query params (e.g., ?id=5&status=active)
        params = request.query_params.dict()

        # 3. Extract the format (default to 'pdf')
        fmt = params.pop("format", "pdf")

        # 4. Generate and return the report as a file download
        try:
            return client.generate_report(report_path, fmt=fmt, params=params)
        except Exception as e:
            return Response({"error": str(e)}, status=400)
```

## 💡 Working with Parameters
The `params` dictionary keys must exactly match the parameter names defined in your `.jrxml` file. For example, if you pass `{"id": 10}`, your Jasper report should have a parameter defined as `$P{id}`.

## ⚖️ License
This project is licensed under the MIT License - see the LICENSE file for details.
clw-csv 0.1.10
CLW (CSV Light Wizard) - A blazing-fast CSV/TSV manipulation tool
Requires Python: >=3.7

# CLW - CSV Light Wizard

> **Pronounced "CLAW"** - because every Rust tool deserves a crab-themed name.

[![Build Status](https://img.shields.io/badge/build-passing-brightgreen)]()
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)]()
[![Rust](https://img.shields.io/badge/rust-1.70%2B-orange.svg)]()

CLW is a blazing-fast CSV manipulation tool written in Rust, designed to make working with CSV files from the command line intuitive and efficient. Born from 10+ years of day-to-day data science work, CLW is a Swiss Army knife that brings together the essential CSV operations you actually need in your workflow.

## Features

- **Lightning Fast**: Built with Rust for maximum performance - See [BENCHMARKS.md](BENCHMARKS.md) for details.
- **Auto-detection**: Automatically detects delimiters (comma, pipe, tab, and space.)
- **Data Analysis**: Built-in statistics and frequency analysis
- **Data Transformation**: Transpose, stack, and paste operations
- **Smart Filtering**: Filter by values 
- **Random Sampling**: Sample rows with reproducible seeds
- **Flexible I/O**: Works with files or stdin/stdout for easy piping
- **Lightweight**: 1.7MB single binary

## Installation

### Pip install (recommended)
```bash
pip install clw-csv
```

### From Source

```bash
git clone https://github.com/sshu2017/clw.git
cd clw/
cargo build --release
sudo cp target/release/clw /usr/local/bin/
```

## Quick Start

```bash
# Pretty-print a CSV with rainbow colors
clw peek data.csv

# Get basic statistics for a column
clw stats --column price products.csv

# Show frequency distribution with a plot
clw freq --column category --plot inventory.csv

# Transpose rows and columns
clw transpose data.csv > transposed.csv

# Stack two CSV files vertically
clw stack file1.csv file2.csv > combined.csv
```

> See the **[USAGE.md](./USAGE.md)** for detailed usage.

##  Acknowledgments

CLW was inspired by:
- [csvkit](https://csvkit.readthedocs.io/) - a comprehensive CSV toolkit in Python
- [csvtool](https://github.com/Chris00/ocaml-csv) - a fast CSV processing tool in OCaml
- [xsv](https://github.com/BurntSushi/xsv) - a CSV toolkit in Rust (now unmaintained)
- [xan](https://github.com/medialab/xan) and [qsv](https://github.com/dathere/qsv) - two feature-rich CSV toolkits in Rust (though larger in size)

Special thanks to the Rust community for excellent libraries:
- [clap](https://github.com/clap-rs/clap) - Command-line argument parsing
- [csv](https://github.com/BurntSushi/rust-csv) - CSV reading/writing
- [colored](https://github.com/mackwic/colored) - Terminal colors

## Star History

If you find CLW useful, please consider giving it a star on GitHub.
iflow-mcp_saishridhar-webscraper 0.1.3
Add your description here
Requires Python: >=3.10

# Webscraper MCP
An MCP server for claude desktop that allows claude to scrape text on websites by providing claude the link. It can also scrape transcripts from youtube links and pdfs from pdf links. 

## Tools available:

#### get_pdf
Convert a URL that leads to a PDF file to markdown text. Args: input_url (str): Path to the PDF file to convert Returns: str: markdown_text



#### get_webpage_content
Returns the text content on a webpage based on the link provided. Using this tool you can access links provided by the user so you don't have deny those requests. When the user provides a webpage link which is NOT a youtube or github link and asks questions based on that, this function should be called. Args: url: The url from which you want to text to be extracted.



#### get_youtube_transcript
Use this tool when you receive youtube links from the user. This tool will extract the transcript from the youtube video and return it to you. Therefore if a user asks questions on a youtube video after providing a link, you can answer their question with this tool. Args: url: The url from which you want to text to be extracted.





Certified by MCPReview<br>
https://mcpreview.com/mcp-servers/saishridhar/webscraper