🎯 Tutorial 4: Tool Using Agent

Welcome to the world of tools! This tutorial teaches you how to create agents that can use different types of tools to perform specific tasks. This is where your agents become truly powerful and capable of real-world actions.

🎯 What You'll Learn

• Built-in Tools: Using Google ADK's pre-built capabilities
• Function Tools: Creating custom Python functions as tools
• Third-party Tools: Integrating with LangChain, CrewAI, and other frameworks
• MCP Tools: Integration with Model Context Protocol

🧠 Core Concept: Tools in ADK

Tools are functions that your agent can call to perform specific tasks. Think of them as the agent's "hands" - they allow the agent to:

• Search the web and access real-time information
• Execute code and perform calculations
• Call external APIs and services
• Access databases and file systems
• Interact with other AI frameworks

🔧 Types of Tools in ADK

1. Built-in Tools

Google ADK provides powerful pre-built tools:

• Search Tool: Web search capabilities
• Code Execution Tool: Run Python code safely
• RAG Tools: Retrieval-augmented generation
• Cloud Tools: Google Cloud integrations

Note: Built-in tools work only with Gemini models

2. Function Tools

Custom Python functions you create:

• Mathematical calculations
• Data processing
• API calls
• File operations
• Business logic

3. Third-party Tools

Integration with other frameworks:

• LangChain Tools: Web scraping, document loaders, etc.
• CrewAI Tools: Specialized agent tools
• Custom Integrations: Any external service

4. MCP Tools

Integration with Model Context Protocol:

• External MCP Servers: Connect to existing MCP servers
• Custom MCP Servers: Create your own MCP server
• Protocol Communication: SSE and Streamable HTTP support

🚀 Tutorial Structure

This tutorial contains four comprehensive examples:

📍 Example 1: Built-in Tools

Location: ./4_1_builtin_tools/

• Learn to use Google ADK's pre-built tools
• Implement web search capabilities
• Explore code execution tools

📍 Example 2: Function Tools

Location: ./4_2_function_tools/

• Create custom Python functions as tools
• Build mathematical and utility tools
• Implement API integration tools

📍 Example 3: Third-party Tools

Location: ./4_3_thirdparty_tools/

• Integrate LangChain tools
• Use CrewAI specialized tools
• Create custom integrations

📍 Example 4: MCP Tools

Location: ./4_4_mcp_tools/

• Connect to Model Context Protocol servers
• Use filesystem and Wikipedia MCP tools
• Create custom MCP servers

📁 Project Structure

4_tool_using_agent/
├── README.md                    # This tutorial overview
├── 4_1_builtin_tools/          # Built-in tools examples
├── 4_2_function_tools/         # Function tools examples  
├── 4_3_thirdparty_tools/       # Third-party tools examples
└── 4_4_mcp_tools/              # MCP tools examples

Each example directory follows the standard structure:

• Python file: Contains the agent implementation and Streamlit app
• README.md: Setup and usage documentation
• requirements.txt: Dependencies list

🎯 Learning Objectives

By the end of this tutorial, you'll understand:

• ✅ How to use Google ADK's built-in tools effectively
• ✅ How to create and integrate custom function tools
• ✅ How to leverage third-party tool ecosystems
• ✅ How to connect to and create MCP servers
• ✅ When to use each type of tool
• ✅ Best practices for tool design and integration

💡 Pro Tips

• Start with Built-ins: Use Google's tools when possible - they're optimized
• Clear Descriptions: Write detailed docstrings for your tools
• Error Handling: Always handle potential errors in your tools
• Tool Selection: Help the AI understand when to use each tool
• Testing: Test each tool independently before combining

🎯 Real-World Applications

Tool-using agents are essential for:

• Information Retrieval: Search engines, knowledge bases
• Data Analysis: Processing and analyzing data
• API Integration: Connecting to external services
• Automation: Performing repetitive tasks
• Decision Making: Using external data for decisions

🚨 Important Notes

• Model Compatibility: Built-in tools only work with Gemini models
• Tool Mixing: Cannot mix built-in and custom tools in same agent
• Performance: Built-in tools are optimized for speed
• Security: Custom tools require proper validation