semvec/pypi-packages huggingface dataset:
universal-mcp-outlook 0.1.15
A Universal MCP Application: universal_mcp_outlook
License: MIT
Requires Python: >=3.11

# Outlook Universal MCP Server

This repository contains an implementation of an Outlook Universal MCP (Model Context Protocol) server. It provides a standardized interface for interacting with Outlook's tools and services through a unified API.

The server is built using the Universal MCP framework.

This implementation follows the MCP specification, ensuring compatibility with other MCP-compliant services and tools.

## Usage

You can start using Outlook directly from [agentr.dev](https://agentr.dev). Visit [agentr.dev/apps](https://agentr.dev/apps) and enable Outlook.

If you have not used universal mcp before follow the setup instructions at [agentr.dev/quickstart](https://agentr.dev/quickstart)

## Available Tools

The full list of available tools is at [./src/universal_mcp_outlook/README.md](./src/universal_mcp_outlook/README.md)

## Local Development

### 📋 Prerequisites

Ensure you have the following before you begin:

- **Python 3.11+** (recommended)
- **[uv](https://github.com/astral-sh/uv)** (install globally with `pip install uv`)

### 🛠️ Setup Instructions

Follow the steps below to set up your development environment:

1. **Sync Project Dependencies**

   ```bash
   uv sync
   ```

   This installs all dependencies from `pyproject.toml` into a local virtual environment (`.venv`).

2. **Activate the Virtual Environment**

   For Linux/macOS:

   ```bash
   source .venv/bin/activate
   ```

   For Windows (PowerShell):

   ```powershell
   .venv\Scripts\Activate
   ```

3. **Start the MCP Inspector**

   ```bash
   mcp dev src/universal_mcp_outlook/server.py
   ```

   This will start the MCP inspector. Make note of the address and port shown in the console output.

4. **Install the Application**
   ```bash
   mcp install src/universal_mcp_outlook/server.py
   ```

## 📁 Project Structure

```text
.
├── src/
│   └── universal_mcp_outlook/
│       ├── __init__.py       # Package initializer
│       ├── server.py         # Server entry point
│       ├── app.py            # Application tools
│       └── README.md         # List of application tools
├── tests/                    # Test suite
├── .env                      # Environment variables for local development
├── pyproject.toml            # Project configuration
└── README.md                 # This file
```

## 📄 License

This project is licensed under the [MIT License](LICENSE).

---

_Generated with **MCP CLI** — Happy coding! 🚀_
tsq-lib 0.2.1
time series quality library
License: MIT
Requires Python: >=3.9

# 📊 Time Series Quality (TSQ) Data Card Generator

A Python tool to automatically generate **data cards** (JSON, HTML, LaTeX) from tidy time-series data in the shape: [timestamp, entity, variable, value]

The data card summarizes dataset characteristics, data quality, and per-variable/per-entity statistics.  
Useful for **finance, environmental, health, energy, or any temporal datasets**.

---

## ✨ Features

- Dataset overview (observations, entities, variables, time range, inferred frequency).
- Data quality checks (missing values, duplicates, timestamp monotonicity).
- Variable-level stats (obs, missing, min, max).
- Entity-level breakdown with variable stats.
- Export to:
  - **JSON** (machine-readable)
  - **HTML** (human-readable data card)
  - **LaTeX** (academic reporting)

---

## 📦 Installation

Install directly from PyPI:

```bash
pip install tsq-lib
```


## 🚀 Usage

```python
import pandas as pd
from tsq.data_card import save_data_card_json, save_data_card_html, save_data_card_latex

# Example dataset in tidy format
data = {
    "timestamp": pd.date_range("2025-01-01", periods=5, freq="D").repeat(2),
    "entity": ["AAPL"]*5 + ["MSFT"]*5,
    "variable": ["close"]*10,
    "value": [170, 171, 169, 172, None, 310, 312, 309, 315, 318]
}
df = pd.DataFrame(data)

# Generate JSON, HTML, and LaTeX data cards
save_data_card_json(df, "Example Equity Dataset", "data_card.json")
save_data_card_html(df, "Example Equity Dataset", "data_card.html")
save_data_card_latex(df, "Example Equity Dataset", "data_card.tex")

print("✅ Data card reports saved.")
```

### 📂 Outputs

- **data_card.json** → machine-readable stats  
- **data_card.html** → clean, human-readable data card  
- **data_card.tex** → LaTeX table & section for papers  


## 📖 Examples

This package can be applied across domains. Two example workflows are included:

- **Finance Example**  
  Demonstrates generating a data card for equity price data (e.g. AAPL, MSFT close prices).  
  See: `examples/finance`

- **Air Quality Example**  
  Demonstrates transforming the UCI Air Quality dataset into tidy format and generating a data card with per-variable statistics.  
  See: `examples/air_quality`

## Acknowledgments

Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Research Executive Agency (REA). Neither the European Union nor the granting authority can be held responsible for them.

![EU Logo](images/eu_funded_logo.jpg)


## License

MIT — see LICENSE.