> ## Documentation Index > Fetch the complete documentation index at: https://praison.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. # MCP with Streamlit > How to properly integrate MCP (Model Context Protocol) tools with PraisonAI Agents in Streamlit applications # MCP with Streamlit This guide explains how to properly integrate MCP (Model Context Protocol) tools with PraisonAI Agents in Streamlit applications, addressing common issues and providing working solutions. ## Common Issues When integrating MCP tools with Streamlit, users often encounter these problems: ### Issue #1: Agent Re-initialization **Problem**: Agent gets re-initialized on every Streamlit interaction, causing MCP tools to fail. **Solution**: Use Streamlit's session state to initialize the agent only once. ### Issue #2: LLM Provider Format **Problem**: Using provider/model format like `"ollama/llama3.2"` can cause tool calling issues in Streamlit environments. **Solution**: Use standard model names like `"gpt-4o-mini"` instead of provider/model format. ### Issue #3: Missing Error Handling **Problem**: No user feedback when MCP initialization fails. **Solution**: Implement comprehensive error handling with user-friendly messages. ## Working Example Here's a complete working example that demonstrates the correct approach: ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} import streamlit as st from praisonaiagents import Agent from praisonaiagents import MCP import traceback st.title("🏠 AI Airbnb Assistant") # Configuration in sidebar with st.sidebar: st.header("⚙️ Configuration") # Use standard model names, not provider/model format llm_model = st.selectbox( "Choose LLM Model", options=[ "gpt-4o-mini", # ✅ Correct format "gpt-4o", "claude-3-5-sonnet-20241022" ], index=0 ) debug_mode = st.checkbox("Enable Debug Mode", value=False) # Initialize session state (CRITICAL for Streamlit) if "agent_initialized" not in st.session_state: st.session_state.agent_initialized = False st.session_state.agent = None st.session_state.init_error = None # Function to initialize agent with proper error handling def initialize_agent(): try: with st.spinner("🔄 Initializing AI agent..."): # Create MCP tools mcp_tools = MCP( "npx -y @openbnb/mcp-server-airbnb --ignore-robots-txt", timeout=60, debug=debug_mode ) # Create agent - this should only happen ONCE agent = Agent( instructions="You are a helpful Airbnb assistant...", llm=llm_model, # Use standard format tools=mcp_tools, verbose=debug_mode ) return agent, None except Exception as e: error_msg = f"Failed to initialize agent: {str(e)}" if debug_mode: error_msg += f"\n\nFull traceback:\n{traceback.format_exc()}" return None, error_msg # Agent initialization (only runs once) if not st.session_state.agent_initialized: if st.button("🚀 Initialize AI Assistant", type="primary"): agent, error = initialize_agent() if agent: st.session_state.agent = agent st.session_state.agent_initialized = True st.session_state.init_error = None st.success("✅ AI Assistant initialized successfully!") st.rerun() else: st.session_state.init_error = error st.error(f"❌ Initialization failed: {error}") # Main interface (only show if agent is ready) if st.session_state.agent_initialized and st.session_state.agent: query = st.text_input("🔍 What are you looking for?") if st.button("Search") and query: try: with st.spinner("🔍 Searching..."): # Use the SAME agent instance from session state result = st.session_state.agent.start(query) st.write(result) except Exception as e: st.error(f"❌ Search failed: {str(e)}") ``` ## Best Practices ### 1. Session State Management Always use Streamlit's session state to manage agent lifecycle: ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # ✅ Correct - Initialize once in session state if "agent" not in st.session_state: st.session_state.agent = Agent(...) # ❌ Wrong - Re-initializes on every interaction agent = Agent(...) ``` ### 2. LLM Model Selection Use standard model names, avoid provider/model format in Streamlit: ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # ✅ Correct - Standard model names llm="gpt-4o-mini" llm="claude-3-5-sonnet-20241022" # ❌ Problematic in Streamlit - Provider/model format llm="ollama/llama3.2" llm="anthropic/claude-3-5-sonnet" ``` ### 3. Error Handling Implement comprehensive error handling: ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} try: # MCP initialization mcp_tools = MCP("your-mcp-command") agent = Agent(tools=mcp_tools, ...) except Exception as e: st.error(f"Initialization failed: {str(e)}") # Provide troubleshooting tips ``` ### 4. User Feedback Provide clear feedback during initialization: ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} with st.spinner("🔄 Initializing AI agent and MCP tools..."): # Initialization code here pass if initialization_successful: st.success("✅ AI Assistant initialized successfully!") else: st.error("❌ Initialization failed") ``` ## Troubleshooting ### MCP Tools Not Found If you get "MCP tool cannot be found" errors: 1. **Check MCP Server Command**: Ensure the MCP server command is correct and the server starts successfully 2. **Verify Dependencies**: Make sure all required dependencies (Node.js, npm, etc.) are installed 3. **Test Manually**: Try running the MCP server command manually first 4. **Enable Debug Mode**: Set `debug=True` in MCP constructor for detailed logs ### Tool Calling Issues If tools aren't being called properly: 1. **Use Standard LLM Format**: Avoid provider/model format like `"ollama/llama3.2"` 2. **Check API Keys**: Ensure proper environment variables are set for your chosen LLM 3. **Session State**: Verify agent is properly stored in session state 4. **Timeout Settings**: Increase timeout if needed: `MCP(command, timeout=120)` ### Threading Conflicts If you encounter threading-related errors: 1. **Single Agent Instance**: Use only one agent instance per session 2. **Proper Cleanup**: Let Streamlit handle cleanup naturally 3. **Avoid Manual Threading**: Don't create additional threads in Streamlit ## Complete Working Example For a complete, production-ready example, see: * [`examples/python/ui/mcp-streamlit-airbnb.py`](https://github.com/MervinPraison/PraisonAI/blob/main/examples/python/ui/mcp-streamlit-airbnb.py) This example includes: * ✅ Proper session state management * ✅ Comprehensive error handling * ✅ User-friendly interface * ✅ Debug mode support * ✅ Troubleshooting guidance * ✅ Configuration options ## Environment Setup Make sure your environment has the required dependencies: ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # Install PraisonAI Agents pip install praisonaiagents # Install Streamlit pip install streamlit # For Airbnb MCP server example npm install -g @openbnb/mcp-server-airbnb # Set environment variables for your chosen LLM export OPENAI_API_KEY="your-key" # or export ANTHROPIC_API_KEY="your-key" ``` ## Running the Example ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # Run the working example streamlit run examples/python/ui/mcp-streamlit-airbnb.py # Or create your own based on the patterns above streamlit run your-mcp-app.py ``` ## Summary The key to successfully using MCP with Streamlit is: 1. **Proper session state management** - Initialize agent only once 2. **Standard LLM format** - Avoid provider/model format in Streamlit 3. **Comprehensive error handling** - Provide user feedback 4. **Correct usage patterns** - Follow Streamlit best practices By following these guidelines, MCP tools will work reliably in your Streamlit applications without requiring any modifications to the core MCP implementation.