Unconstrained tool calls from open-weight LLMs fail far too often in multi-turn agent workflows. Testing Kimi K2.6 on vLLM 0.28 showed a 92 percent schema pass rate, meaning 1 in 12.5 tool calls produced invalid JSON payloads that corrupted agent contexts.
Server-side constrained decoding in vLLM 0.31 solves this structural problem by applying grammar masks at token generation time, elevating schema compliance to 100 percent without prompt engineering.
However, grammar masking only enforces syntax, not semantic accuracy. Engineering teams must still validate parameter values downstream while managing the CPU overhead of compiling complex schema state machines.
Read the full article: vLLM Constrained Decoding: Fix Tool Call Schema Errors
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