semvec/pypi-packages huggingface dataset:
universal-mcp-semrush 0.1.1
A Universal MCP Application: universal_mcp_semrush
License: MIT
Requires Python: >=3.11

# Semrush Universal MCP Server

This repository contains an implementation of an Semrush Universal MCP (Model Context Protocol) server. It provides a standardized interface for interacting with Semrush'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 Semrush directly from [agentr.dev](https://agentr.dev). Visit [agentr.dev/apps](https://agentr.dev/apps) and enable Semrush.

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 [Tools](./src/universal_mcp_semrush/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_semrush/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_semrush/server.py
   ```

## 📁 Project Structure

```text
.
├── src/
│   └── universal_mcp_semrush/
│       ├── __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! 🚀_
Tasklin 0.0.1
A unified CLI for AI providers like OpenAI, Ollama and more
License: MIT
Requires Python: >=3.8

# Tasklin

Tasklin is a versatile Python CLI for leveraging multiple AI providers like OpenAI, Ollama, Anthropic (Claude), and more. It’s designed for developers and creators who want to integrate AI into their workflows, pipelines, or automation, without being limited to “chatting” with AI.

Use it to generate text, process data, or build AI-driven scripts, all directly from your terminal.
---

## Features

* Single CLI to interact with multiple AI providers
* Supports **OpenAI**, **Ollama**, **Anthropic(Claude)**, **DeepSeek** and more in the future
* Returns structured responses with tokens used and execution time
* Supports **sync** and **async** execution
* Clean error handling for missing models, invalid API keys, etc.
* Available on **PyPI** as `tasklin`

---

## Installation

Clone the repo and install dependencies:

```bash
git clone https://github.com/jetroni/tasklin.git
cd tasklin
python -m venv .venv
source .venv/bin/activate   # On Windows: .venv\Scripts\activate
pip install -r requirements.txt
```

Or install directly from PyPI:

```bash
pip install tasklin
```

(Optional) Make the CLI globally executable:

```bash
pip install --editable .
```

---

## Usage

Basic usage:

```bash
tasklin --type openai --prompt "Hello AI"
```

Run with async mode:

```bash
tasklin --type openai --prompt "Hello AI" --async-mode
```

Ollama example (with base URL):

```bash
tasklin --type ollama --base-url http://localhost:11434 --model codellama --prompt "Hello"
```

---

## CLI Options

| Option         | Description                                       |
| -------------- | ------------------------------------------------- |
| `--type`       | AI provider type (openai, ollama)                 |
| `--prompt`     | Text prompt to send to the AI                     |
| `--key`        | API key for providers that require authentication |
| `--model`      | Model name to use (defaults vary per provider)    |
| `--base-url`   | Base URL for self-hosted providers like Ollama    |
| `--async-mode` | Run asynchronously (flag, no value needed)        |

---

## Response Structure

All responses are returned as a structured JSON object:

```json
{
  "provider": "openai",
  "model": "gpt-4o-mini",
  "output": "Hello! How can I assist you today?",
  "raw": {...},
  "tokens_used": 17,
  "duration_ms": 543
}
```

---

## Contributing

We welcome contributions!

* Add support for new AI providers
* Improve error handling
* Enhance async execution and CLI experience

Please fork the repo and submit a pull request.

---

## License

MIT License. Free to use and modify.

---

## PyPI

tasklin is published on PyPI. Install via:

```bash
pip install tasklin
```

Stay up to date with the latest releases and check the package page: [https://pypi.org/project/tasklin/](https://pypi.org/project/tasklin/)