How to Test Tool-Calling Accuracy in AI Agents

AI agent tool-calling evaluation should distinguish between tool selection errors, such as choosing an unnecessary or incorrect function, and argument errors, where the selected tool receives malformed or semantically wrong inputs. The guide outlines three complementary methods: reference-free LLM judges for context-dependent cases with multiple potentially valid choices, deterministic JSON Schema and value checks for known structural or expected-input requirements, and trajectory comparison for multi-step workflows where call order or resulting state may matter. It recommends combining these methods as appropriate, including no-tool scenarios and full arrays of calls rather than only the first call. For fair cross-model comparisons through OpenRouter, evaluators should keep prompts, tools, test cases, grading rules, reasoning and sampling settings, and provider routing consistent, validate returned arguments locally, verify model parameter support, and run cases repeatedly to account for variability. It also cautions against overly narrow tests, confusing schema-valid inputs with correct inputs, allowing truncated outputs to distort results, and requiring a single exact trajectory when multiple sequences can achieve an equivalent valid outcome.

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