llm-agent-mcp

# LLM Agent for Weather Determination LLM agent for weather information retrieval through interaction with two APIs via unified MCP interface. ## Description The agent uses: - **Nominatim API** for determining coordinates by city name - **Open-Meteo API** for retrieving weather data by coordinates - **Groq LLM** for natural language processing - **MCP protocol** for unified tool access Supports current weather, forecasts, and historical data in multiple languages. ## Installation and Setup ### Requirements - Python 3.11+ - UV package manager ### Setup 1. Install dependencies: ```bash uv sync ``` 2. Create `.env` file with API key: ``` GROQ_API_KEY=your_groq_api_key_here ``` ### Running Requires 2 terminals: **Terminal 1 - MCP Server:** ```bash uv run python -m app.mcp_server ``` **Terminal 2 - UI:** ```bash uv run streamlit run app/streamlit_app.py ``` ## Usage Examples ``` "What's the weather in London today?" "Weather forecast for next 3 days in Paris" "What was the weather in Tokyo yesterday?" ``` ## Project Structure ``` app/ ├── agents/weather_agent.py # Main agent logic ├── prompts/system_prompt.py # System prompt ├── schemas/ # Pydantic schemas ├── services/ # LLM service and settings ├── tools/ # API tools ├── ui/weather_ui.py # Streamlit interface ├── mcp_server.py # MCP server └── streamlit_app.py # Entry point ``` ## Technical Details - **LLM**: Groq (llama3-8b-8192) - **Framework**: LangChain + LangGraph - **Pattern**: ReAct (Reasoning + Acting) - **UI**: Streamlit - **APIs**: Nominatim (OpenStreetMap) + Open-Meteo