Tool Use Pattern

Tool use transforms AI agents from knowledge systems into action-takers. By connecting to external tools, agents can search the web, execute code, query databases, and interact with APIs.

How Tool Use Works

Modern LLMs are trained to recognize when they need external tools and how to call them:

# Defining tools for an agent
tools = [
    {
        "name": "web_search",
        "description": "Search the web for current information",
        "parameters": {
            "query": {"type": "string", "description": "Search query"}
        }
    },
    {
        "name": "calculator",
        "description": "Perform mathematical calculations",
        "parameters": {
            "expression": {"type": "string", "description": "Math expression"}
        }
    }
]

# Agent decides which tool to use
response = llm.generate(
    prompt=user_query,
    tools=tools,
    tool_choice="auto"  # Let the model decide
)

The Tool Calling Flow

1. User request → Agent receives task
2. Tool selection → Agent decides if/which tool to use
3. Parameter extraction → Agent formats the tool call
4. Execution → System runs the tool
5. Result integration → Agent incorporates result into response

Common Tool Categories

Category	Examples	Use Cases
Information	Web search, Wikipedia, news APIs	Research, fact-checking
Computation	Calculator, code interpreter	Math, data analysis
Data	SQL queries, file operations	Business intelligence
Communication	Email, Slack, calendar	Automation workflows
External Services	Weather, maps, translation	Real-world integration

MCP: The New Standard

The Model Context Protocol (MCP), now supported by ChatGPT, Gemini, and Copilot, provides a standardized way to connect tools:

// MCP server example
const server = new MCPServer({
  tools: [{
    name: "get_customer",
    description: "Fetch customer data by ID",
    inputSchema: { customerId: "string" }
  }]
});

2025 Update: Over 10,000 MCP servers are now active, with adoption across major AI platforms.

Best Practices

• Clear descriptions: Help the model understand when to use each tool
• Validate inputs: Check parameters before execution
• Handle failures: Tools can timeout or return errors
• Limit scope: Only expose necessary capabilities

Next, we'll learn how agents break down complex tasks through planning. :::