A coding agent must emit a valid tool call--a parseable invocation of a tool in the provided schema--before the harness can execute its chosen action.
We study how local serving stacks affect this protocol step and show that measured outcomes can depend on the serving layer rather than model behavior alone.
In Ollama
The default tools= request is gated per model by a static template flag: some models are accepted and return calls as text, some return native tool_calls, while Phi-3 and Gemma-3 are rejected before inference.
In our harness
Rejection and retry exhaustion are not preserved as structured failure metadata, so downstream analysis can misclassify them as model non-calls and naively report 0% fidelity.
Adding a text tool list while retaining the native channel recovers much of the measured fidelity for accepted models, whereas a uniform text protocol reduces fidelity for Llama-3.2, which has native tool-call support.
Cross-stack probes
Ollama, llama.cpp, vLLM, and SGLang show different handling of the same request.
Constrained decoding
Removes parse failures but can induce non-termination, and turn-pooled versus per-instance estimates differ by up to about 55 points.
Conclusion
We conclude with a checklist for treating serving behavior as part of the evaluation protocol.