Skill Guide

Function calling and tool-use APIs (OpenAI, Anthropic, Google Gemini)

Function calling and tool-use APIs are programmatic interfaces that enable large language models (LLMs) to invoke external functions, APIs, or tools by generating structured requests based on natural language input, with providers like OpenAI, Anthropic, and Google Gemini offering standardized but distinct implementations.

This skill allows developers to extend LLM capabilities beyond text generation, enabling real-world actions like database queries, calculations, or third-party service integrations, which directly increases product utility and automation potential. Organizations leveraging it build more intelligent, autonomous systems that reduce manual intervention and improve operational efficiency.

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15% Avg AI Risk

How to Learn Function calling and tool-use APIs (OpenAI, Anthropic, Google Gemini)

Focus on understanding the core pattern: the LLM generates a JSON object to call a function, receives the result, and formulates a response. Start by learning JSON schema definition and basic Python/Node.js client setup for one provider (e.g., OpenAI).

Practice defining and managing multiple functions for a coherent workflow, handling function execution errors gracefully, and managing conversation context with tool results. Avoid the common mistake of poorly defining function parameters, which leads to ambiguous or incorrect LLM calls.

Master orchestration strategies like chaining multiple tool calls, implementing fallback mechanisms, and designing for stateful, multi-step agent loops. Focus on performance optimization, security hardening against prompt injection via tool inputs, and aligning tool architectures with business process automation goals.

Practice Projects

Beginner

Project

Build a Currency Converter Assistant

Scenario

Create a chat interface where the LLM uses a function to fetch real-time exchange rates from a public API and performs currency conversion for the user.

How to Execute

1. Define a Python function (e.g., `get_exchange_rate(from_currency, to_currency)`) that calls an API like exchangerate-api.com. 2. Define its JSON schema for the OpenAI API, specifying parameters. 3. Use the OpenAI ChatCompletion API, passing the function schema in the `functions` parameter. 4. Parse the LLM's function call response, execute your Python function, and send the result back to the LLM for a natural language summary.

Intermediate

Project

Personalized Restaurant Recommendation Bot

Scenario

Build a bot that queries a database (e.g., via a simulated API) for restaurants, filters by user-stated preferences (cuisine, price, location), and returns top picks with details.

How to Execute

1. Define multiple functions: `search_restaurants(cuisine, price_range, neighborhood)` and `get_restaurant_details(restaurant_id)`. 2. Implement the functions to query a structured data source (CSV, SQL, or a mocked REST API). 3. Program the LLM workflow: first, call the search function based on user input; then, call the details function for the top result; finally, synthesize the information into a helpful response. 4. Handle edge cases like no results or ambiguous user preferences by prompting for clarification.

Advanced

Project

Automated Code Review and Refactoring Agent

Scenario

Design a system where an LLM agent analyzes a code snippet, uses tools to check style (linter), run tests (test runner), and then suggests and optionally applies refactoring changes via a code modification API.

How to Execute

1. Define tools for: running a linter (e.g., pylint), executing a test suite (e.g., pytest), and reading/writing to a code repository (e.g., via a GitHub API wrapper). 2. Implement an agent loop where the LLM analyzes code, decides which tool(s) to call, interprets results, and iteratively refines its review or refactoring plan. 3. Incorporate safety checks: require human approval for actual code modifications. 4. Optimize for context management to handle large codebases and ensure the agent doesn't hallucinate tool outputs.

Tools & Frameworks

LLM Provider SDKs & APIs

OpenAI Python/Node.js Library (with function calling)Anthropic Claude API (with tool use)Google Gemini API (with function calling)

The primary interfaces for implementing tool use. Each requires defining functions in a provider-specific JSON schema format and handling the structured request/response cycle in your application code.

Agent Frameworks & Orchestration

LangChain (LangGraph for agents)AutoGenCrewAI

Higher-level abstractions for building complex, multi-step agentic systems. They manage conversation memory, tool execution loops, and agent coordination, reducing boilerplate code for advanced use cases.

API & Data Tools

Postman (API development)SQLite/PostgreSQL (for local data tools)Pandas (data analysis tools)

Used to build and test the actual functions (tools) that the LLM will call. Postman helps design and debug API-based tools; databases and pandas are essential for creating data query and manipulation tools.

Interview Questions

Answer Strategy

The candidate must demonstrate hands-on experience with both APIs. Strategy: Outline the structural differences (e.g., OpenAI uses a `functions` array in the request and a `function_call` object in the assistant message; Anthropic uses a `tools` array and `tool_use` content blocks in the response). Then, discuss the practical solution: abstract the provider-specific logic behind a unified interface or adapter pattern in your code, allowing seamless switching or failover between providers.

Answer Strategy

This tests system design and security awareness. Strategy: Emphasize defining tools with strict, input-validating parameters (e.g., requiring an `order_id` and `reason` for refunds). Implement a critical step: the execution layer (your backend) must perform its own authorization checks *after* the LLM requests a tool call but *before* executing it. Describe an agent loop where high-stakes actions (like refunds) either require a confirmation step or are routed through a separate approval workflow.