# Function Calling (Tool Calling)

> Function calling lets an LLM request structured invocations of your code: describe tools with schemas, the model emits typed calls, your app executes them.

**Function calling (tool calling) is the mechanism that lets language models act: you declare functions with JSON-schema parameters, the model responds with a structured call — name plus typed arguments — and your application executes it and feeds back the result.**

It's the bridge from text generation to action, and the atom every [agent](/glossary/ai-agent) loop is built from: model emits call → app executes → result returns as an observation → model decides the next step. The model never runs anything itself; it produces *intentions* shaped by your schemas, which is exactly why the engineering quality lives in those schemas — sharp names, described parameters, disjoint purposes ([tool-definition-generator](/skills/api/tool-definition-generator) automates the shape).

Production reliability has one golden rule: **feed errors back as observations.** A failed call isn't an exception to crash on — it's information the model uses to retry correctly ("invalid date format" → reformatted call). That pattern, plus validation, idempotency, and permission gating, is the substance of [Production Tool & Function Calling](/guides/concepts/production-tool-calling). The [Model Context Protocol](/glossary/model-context-protocol) standardizes the layer above: where tools come from and how clients discover them.

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_Source: https://agentscamp.com/glossary/function-calling — Term on AgentsCamp._