Function Schema

A Function Schema describes the signature of a callable function or tool that an AI model can invoke — its name, purpose, input parameters with types and constraints, and expected output. Function schemas enable function calling (also called tool use), the pattern where LLMs decide when to call external functions, generate the parameters, and incorporate the results into their reasoning. A function schema for "check inventory" might specify: name ("check_inventory"), description ("look up current stock for a product"), parameters (product_id string required, warehouse_id string optional), and return type (number of units). OpenAI, Anthropic, Google Gemini, Mistral, and most other major LLMs support function schemas in standardized formats. The Model Context Protocol (MCP) is increasingly standardizing how function schemas are shared across AI tools. Tools supporting function schemas include LangChain, LlamaIndex, all major LLM SDKs, and platforms like Pydantic AI and Instructor. AI governance, AI compliance, and AI risk management programs treat function schemas as governed APIs — supporting responsible AI through controlled tool access across enterprise AI agent deployments.

Centralpoint Governs Function Schemas as AI Tools: Oxcyon's Centralpoint AI Governance Platform manages function schemas across OpenAI, Gemini, Llama, and embedded models — keeping schemas and skills on-premise. Centralpoint meters every tool invocation and embeds function-calling chatbots into your portals via a single line of JavaScript.


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