Tool Calling
Tool calling lets a model request a named outside tool in a set format. The request includes arguments. The host app decides whether and how to run it.
Another example
A travel agent asks get_flights for trips to TPE on chosen dates after the user allows search. The host checks permission and arguments, runs the tool, and returns the source. A model request is not proof of execution or permission for a real-world side effect.
Main parts
- 01tool definition
- 02structured arguments
- 03host authorization and result
Use it when
Use this term when an AI needs structured access to outside data or actions that the host checks and authorizes.
Do not use it when
Do not offer powerful tools without the right safeguards. Use least privilege, argument checks, needed confirmation, safe retries, and audit records that match the risk.
Name used in code
model request → host validation → tool result Before you ship
Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback.
Outcome: Use or evaluate Tool Calling to make AI behavior measurable and tied to evidence. User context: A travel agent asks get_flights for trips to TPE on chosen dates after the user allows search. The host checks permission and arguments, runs the tool, and returns the source. A model request is not proof of execution or permission for a real-world side effect. AI method or concept: Tool Calling. Why it fits: Use this term when an AI needs structured access to outside data or actions that the host checks and authorizes. Do not use it when: Do not offer powerful tools without the right safeguards. Use least privilege, argument checks, needed confirmation, safe retries, and audit records that match the risk. AI requirements: Define the input and source evidence. Set model and tool permissions. Use versioned evaluation data and measures. Define failure, decline-to-answer, privacy, speed, and cost limits. Operational safeguards: Keep a clear trace and hide sensitive log data. Show users a safe fallback. Mark steps that need human review. Define how to roll back the model or prompt. Acceptance criteria: Record baseline and target measures on a versioned evaluation set. Test edge cases and hostile inputs. Verify fallback, monitoring, permissions, and rollback. Evidence and limits (evidence boundary): No single official standard controls the whole term. Its meaning may change across teams, platforms, or frameworks. Unknowns to confirm: Target task, model and version, evaluation owner, source data, risk limit, tool permissions, and production fallback.
Check this request
Review the current use of Tool Calling. Definition: Tool calling lets a model request a named outside tool in a set format. The request includes arguments. The host app decides whether and how to run it. Release checks: Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback. Before changing code, report the evidence you found, gaps, severity, and the smallest safe fix.