Skill Guide

LLM function/tool calling APIs (OpenAI, Anthropic, Google Gemini)

A standardized interface allowing developers to pass structured function or tool schemas to LLMs, enabling the model to return structured arguments for calling external code, APIs, or systems.

This skill is critical for building autonomous AI agents and complex workflows, directly reducing manual integration effort and enabling scalable, intelligent automation. It transforms LLMs from text generators into orchestration engines that drive business process efficiency and new product capabilities.

1 Careers

1 Categories

9.2 Avg Demand

15% Avg AI Risk

How to Learn LLM function/tool calling APIs (OpenAI, Anthropic, Google Gemini)

1. Master JSON Schema for defining function parameters. 2. Understand the core request/response flow for a single function call in one API (e.g., OpenAI). 3. Learn to parse the model's structured output and execute the corresponding local code.

1. Implement multi-tool calling scenarios where the model must choose and sequence tools. 2. Build robust error handling and retry logic for function execution failures. 3. Design clear, unambiguous function descriptions and parameter definitions to minimize model hallucination. Common mistake: creating overly complex or ambiguous tool descriptions.

1. Architect agentic systems with tool-use loops, planning, and memory. 2. Optimize for cost and latency by batching calls and managing context. 3. Implement cross-provider abstraction layers and design evaluation frameworks for tool-use accuracy and safety. Mentoring involves reviewing schema design and agent architecture.

Practice Projects

Beginner

Project

Single-Function Weather Lookup

Scenario

Build a CLI tool that uses an LLM to fetch current weather data for a user-specified city.

How to Execute

1. Define a `get_current_weather` function with `location` and `unit` parameters using JSON Schema. 2. Send a user prompt to the API (e.g., OpenAI) with the tool defined. 3. Upon receiving the tool call, execute a local function that calls a weather API (e.g., OpenWeatherMap). 4. Send the API result back to the LLM as a tool response to get a natural language summary.

Intermediate

Project

Multi-Tool Information Retrieval Agent

Scenario

Create an agent that can answer questions requiring information from both a internal knowledge base (vector store) and an external database.

How to Execute

1. Define two tools: `search_internal_docs(query)` and `query_sales_database(sql_query)`. 2. Implement a loop: prompt LLM with the tools, execute the chosen tool, append the result, and re-prompt. 3. Implement a 'final answer' tool or a stop condition. 4. Add guardrails to validate SQL queries and sanitize search terms.

Advanced

Project

Cross-Provider Agentic Workflow for Customer Support

Scenario

Design a system that autonomously resolves support tickets by querying CRMs, checking inventory, and drafting responses, using different LLM providers for different reasoning tasks.

How to Execute

1. Create an abstract tool interface layer to normalize function definitions for OpenAI, Anthropic, and Gemini APIs. 2. Implement a planning agent that decomposes a ticket into sub-tasks (e.g., 'check order status', 'find product manual'). 3. Route sub-tasks to specialized tools and LLMs (e.g., Anthropic for careful drafting). 4. Build a supervisory agent with a memory module to track state, validate actions, and ensure compliance with support policies before executing final actions (e.g., issuing a refund).

Tools & Frameworks

Software & Platforms

OpenAI API (tool_choice, tools parameters)Anthropic Messages API (tools parameter)Google Gemini API (FunctionDeclaration, tools)LangChain / LlamaIndex (tool abstractions)

Core APIs are essential for direct implementation. Frameworks like LangChain provide higher-level abstractions for tool management and agent loops, useful for rapid prototyping and complex orchestration.

Development & Deployment

Pydantic (for schema generation/validation)TypeScript (for robust schema typing)Vector Databases (Pinecone, Weaviate)Containerization (Docker)

Pydantic models can auto-generate JSON Schema for tools. TypeScript provides strong typing for complex tool schemas. Vector DBs are often a tool target for RAG. Containerization is standard for deploying agentic services.

Interview Questions

Answer Strategy

Demonstrate precise knowledge of the `tools` parameter structure. The answer must include the `type: 'function'` wrapper, the `function` object with `name`, `description`, and the `parameters` object following JSON Schema, including `type: 'object'`, `properties`, and `required` array.

Answer Strategy

This tests system design and safety thinking. A strong answer covers multiple layers: 1) Improving the tool description and parameter schemas (e.g., providing valid table/column names in the description or as enum constraints). 2) Implementing a pre-execution validation layer that parses and sandbox-checks the SQL. 3) Using a 'confirmation' tool pattern where the agent must present the query for user approval before execution. 4) Fine-tuning or selecting a more reliable model for the planning task.