Originally published on AI Tech Connect.
What actually breaks when a small model calls a tool Run a capable open-weight model in the 27 to 32 billion parameter range against a realistic tool suite and the transcript is rarely stupid. It reads the request, works out that a database lookup is needed rather than a calculation, and emits something that looks very much like a function call. The decision layer is fine. The failures cluster elsewhere: a date formatted as 11/08/2026 when the schema wanted 2026-08-11, a currency field carrying GBP_STERLING when the enum only accepts GBP, a nested object flattened into a string, a required field quietly omitted because the model could not infer it and did not want to say so. This is why "it picked the right tool" is the wrong success metric. Tool-selection accuracy is the easy half of the…
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