mcp-gradio-poc

# MCP Gradio Multi-Tool Assistant A Gradio web interface for interacting with a Model Control Protocol (MCP) server. This application uses Claude 3.5 Sonnet to process user queries and invoke specialized tools through the MCP. ## Features - **Image Generation**: Generate images from text descriptions using multiple fallback options: - Hugging Face Inference API - Replicate API - SanaSprint Gradio model - **Weather Information**: Get current weather data for any city using OpenWeatherMap API - **Text Translation**: Translate text between languages (currently using mock implementation) ## Setup 1. Clone this repository ``` git clone <repository-url> cd mcp-gradio-poc ``` 2. Install dependencies ``` pip install -r requirements.txt ``` 3. Create a `.env` file in the project root with the following variables: ``` # Required ANTHROPIC_API_KEY=your_anthropic_api_key # Required for weather tool OPENWEATHER_API_KEY=your_openweather_api_key # Optional for image generation (need at least one) HF_API_TOKEN=your_huggingface_token REPLICATE_API_TOKEN=your_replicate_token ``` 4. Run the application ``` python app.py ``` ## Usage 1. Launch the application with `python app.py` 2. Connect to the MCP server by clicking "Connect" (using the default path "gradio_mcp_server.py") 3. Start chatting with the assistant to use the available tools: - For images: "Generate an image of mountains at sunset" - For weather: "What's the weather in Tokyo?" - For translation: "Translate 'hello' to French" ## Project Structure - `app.py`: Main Gradio interface application - `gradio_mcp_server.py`: MCP server implementation with tool definitions - `requirements.txt`: Dependencies - `.env`: Environment variables and API keys ## Adding New Tools To add new tools to the MCP server, add a new function with the `@mcp.tool()` decorator in `gradio_mcp_server.py`. The function should: 1. Use async/await syntax 2. Have typed parameters with docstrings 3. Return a JSON string with properly formatted results Example: ```python @mcp.tool() async def my_new_tool(param1: str, param2: int = 42) -> str: """Tool description here. Args: param1: Description of parameter 1 param2: Description of parameter 2 (default: 42) """ result = {} # Tool logic here return json.dumps(result) ``` ## Troubleshooting - If image generation fails, make sure you have at least one of the API keys set up - For weather data, an OpenWeatherMap API key is required - For any API errors, check the console output for detailed error messages