MCP-Agent

# MCP Streamlit Agent A Streamlit interface for creating and interacting with LLM agents using MCP tools. Build intelligent assistants with Claude, GPT, or Gemini models and visualize their reasoning in real-time. ## Features - 🤖 Create ReAct agents using Claude, GPT-4o, or Gemini models - 🛠️ Easily configure MCP tools through a user-friendly interface - 📊 Real-time visualization of agent thinking process and tool usage - 🔄 Asynchronous processing for responsive performance - 🧩 Modular code structure for easier maintenance and extensions ## Setup 1. Clone the repository: ```bash git clone https://github.com/sarvottam-bhagat/mcp-streamlit-agent.git cd mcp-streamlit-agent ``` 2. Set up the environment (using Make): ```bash make setup ``` Or manually: ```bash pip install -r requirements.txt cp .env.example .env ``` 3. Edit the `.env` file to add your API keys: ``` GOOGLE_API_KEY=your_google_api_key_here OPENAI_API_KEY=your_openai_api_key_here ANTHROPIC_API_KEY=your_anthropic_api_key_here OPENWEATHER_API_KEY=your_openweather_api_key_here ``` To get an OpenWeatherMap API key: - Sign up at [OpenWeatherMap](https://openweathermap.org/api) - Get your free API key - Add it to the `.env` file ## Usage 1. Start the Streamlit app (with Make): ```bash streamlit run app.py ``` Or using the run script: ```bash ./run.sh ``` 2. Access the web interface at `http://localhost:8501` 3. In the interface: - Configure MCP tools in the sidebar - Choose from Claude, GPT, or Gemini models - Send messages to your agent and see responses in real-time - View detailed tool usage information in expandable sections ## Using MCP Tools The application supports adding various MCP tools through the UI: 1. Click "Add MCP Tools" in the sidebar 2. Add tool configurations in JSON format: ```json { "tool_name": { "command": "command_to_execute", "args": ["arg1", "arg2"], "transport": "stdio" } } ``` 3. Click "Add Tool" button 4. Changes will be applied automatically The application comes with default tools configured in `configs/default.json`. ## Project Structure ``` mcp-streamlit-agent/ ├── app.py # Main Streamlit application ├── utils/ # Utility modules │ ├── callbacks.py # Callback handlers for streaming responses │ ├── event_loop.py # Async event loop management │ └── ui.py # UI-related utility functions ├── configs/ # Configuration files │ └── default.json # Default MCP tool configurations ├── adapters/ # MCP tool adapters │ └── weather_server.py # Example weather tool adapter └── README.md # Project documentation ``` ## How It Works 1. **MCP Tool Integration**: Machine Control Protocol (MCP) allows the agent to interact with various tools using a standardized interface. 2. **Streaming Responses**: Agent responses and tool usage are streamed in real-time to provide immediate feedback. 3. **Model Selection**: Choose from Claude, GPT, or Gemini models to power your agent. ## Dependencies - Streamlit - LangChain & LangGraph - Google Generative AI (Gemini) - OpenAI (GPT) - Anthropic (Claude) - Python dotenv - MCP (Machine Control Protocol) - Requests (for API calls) - OpenWeatherMap API