Agents leverage software tools to extend their functionality, typically through API calls, though some can craft tools on the fly for unique challenges. This blend of using and building tools—augmented by advances like function calling and protocols like Model Context Protocol (MCP)—makes agents versatile problem-solvers. Here’s how they harness tools effectively.

Types of Tools Agents Use

Agents tap into a range of tools, each suited to specific tasks.

• Browser Automation: Tools like Selenium or Puppeteer let agents navigate websites, fill forms, or scrape data with minimal steps.
• Desktop Automation: Using PyAutoGUI or AutoIt, agents interact with desktop apps—clicking buttons or typing text seamlessly.
• API Calling: Agents connect to services via APIs with libraries like Requests, sending queries and handling responses effortlessly.

Function Calling: Precision Tool Interaction

Function calling empowers agents to invoke tools with structured precision, enhancing their ability to act on complex instructions.

• Agents pass specific parameters to predefined functions—like get_weather(city="Tokyo")—and receive targeted outputs, reducing ambiguity.
• This method, common in LLMs like OpenAI’s models, lets agents break tasks into modular steps, such as fetching data then formatting it.
• It bridges natural language and code, enabling an agent to interpret “check the forecast” as a series of precise API calls.

Model Context Protocol (MCP): Contextual Tool Use

The Model Context Protocol (MCP), introduced by Anthropic, connects agents to tools by providing rich context, making tool use smarter.

• MCP feeds agents situational data—like user intent or past actions—via a standardized format, so a travel agent knows to prioritize flights over hotels.
• It integrates with frameworks like LangChain, ensuring tools align with the agent’s goals, not just raw inputs.
• This context-awareness reduces errors, letting agents pick the right tool—like choosing a currency API for a finance task—every time.