Saqr-MCP

# Saqr-MCP  Saqr-MCP is a Python application that implements the Model Context Protocol (MCP) to enable AI assistant capabilities with local models. It provides a client-server architecture where the client communicates with local models through Ollama, while the server provides tools for the LLM ## Features - Interactive chat interface for querying the model - Web search tool integration using DuckDuckGo and Playwright - Local model inference through Ollama - Async architecture for efficient processing - Visual loading animations for better user experience ## Prerequisites - Python 3.11 or higher - [Ollama](https://ollama.ai/) installed with local models - [UV](https://github.com/astral-sh/uv) package manager (recommended) ## Installation 1. Clone this repository: ```bash git clone https://github.com/your-username/saqr-mcp.git cd saqr-mcp ``` 2. Create and activate a virtual environment (optional but recommended): ```bash uv venv # On Windows venv\Scripts\activate # On Unix or MacOS source venv/bin/activate ``` 3. Install dependencies: ```bash uv add -r requirements.txt playwright install chromium ``` 4. Set up environment variables: - Copy `.env.example` to `.env` - Set `MODEL_NAME` to your preferred Ollama model (e.g., `qwen3:1.7b`) ## Usage 1. Make sure Ollama is running with your chosen model available 2. Run the client: ```bash python main.py ``` 3. Type your queries in the interactive console: ``` MCP Client Started! Type your queries or 'quit' to exit. Query: ``` 4. Type `quit` to exit the application ## Project Structure - `main.py` - Entry point that starts the MCP client - `src/` - `client.py` - MCP client implementation connecting to Ollama - `server.py` - MCP server implementation with web search tool - `logger.py` - Custom logging utilities with visual animations ## Available Tools Currently, the server implements the following tools: - **web_search**: Searches the web using DuckDuckGo and extracts relevant content using Playwright. ## Environment Variables | Variable | Description | Default | | ------------ | -------------------------------------------------------- | ------- | | `MODEL_NAME` | The name of the Ollama model to use (e.g., `qwen3:1.7b`) | None | ## Dependencies - `mcp[cli]` - Model Context Protocol implementation - `httpx` - HTTP client for Python - `playwright` - Browser automation library - `duckduckgo-search` - API for DuckDuckGo searches - `loguru` - Python logging made simple - `groq` - Groq API client (optional) - `ollama` - Interface to Ollama for local models ## License [MIT](LICENSE) ## Contributing Contributions are welcome! Please feel free to submit a Pull Request. 1. Fork the repository 2. Create your feature branch (`git checkout -b feature/amazing-feature`) 3. Commit your changes (`git commit -m 'Add some amazing feature'`) 4. Push to the branch (`git push origin feature/amazing-feature`) 5. Open a Pull Request