Unverified50% confidenceFactExact time
LLM tool calling (Tool Calling/Function Calling) is a capability of large models: the model generates structured tool-call requests according to a preset JSON Schema, external code executes them and passes the results back to the model, and the model continues reasoning based on the results.
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50%
Confidence
Long-term
Relevance
10/17/2026
First Seen
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Related Claims
VerifiedThe basic principle of tool calling is that the model identifies scenarios requiring external capabilities, outputs a structured function call request (usually in JSON format), and after the external system executes it, the result is returned to the model to continue reasoning.78% similarUnverifiedCurrent mainstream LLM agents do not execute code or SQL directly; instead, the model outputs structured text (usually JSON) declaring the functions and parameters to be called, which the host program then parses and executes.77% similarUnverifiedThe Tool workflow is model-driven: the user's question is first sent to the LLM, which determines which tool to call, with decision-making authority residing in the model75% similarUnverifiedLLM-Loop (Large Language Model loop working mechanism) refers to embedding an AI large model in a continuously iterating feedback loop: the user states a requirement, the AI generates output, the user validates and provides feedback, and the AI corrects and outputs again, cycling until the goal is reached.74% similarVerifiedDuring inference, large models read tool metadata as context and use semantic understanding to determine whether a tool needs to be called and what parameters to pass, a process known as Function Calling or Tool Use.73% similar
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