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:

   git clone https://github.com/sarvottam-bhagat/mcp-streamlit-agent.git
   cd mcp-streamlit-agent
   

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2. Set up the environment (using Make):

   make setup
   

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   Or manually:

   pip install -r requirements.txt
   cp .env.example .env
   

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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
   

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   To get an OpenWeatherMap API key:

   • Sign up at OpenWeatherMap
   • Get your free API key
   • Add it to the .env file

Usage

1. Start the Streamlit app (with Make):

   streamlit run app.py
   

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   Or using the run script:

   ./run.sh
   

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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:

   {
     "tool_name": {
       "command": "command_to_execute",
       "args": ["arg1", "arg2"],
       "transport": "stdio"
     }
   }
   

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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