LLM agents for structured data analysis rely on dynamic query generation, in which the model synthesises tool calls at runtime. This introduces failures such as fabricated dataset references, unsupported operations, and incorrect results, which existing mitigations reduce but do not eliminate. We propose predefined-tool execution as an architectural alternative in which the language model selects and parameterises tools from a fixed, schema-validated registry. We formalise the approach using typed tool specifications and prove executability and hallucination-suppression properties under assumptions. We complement the architecture with a Numerical Faithfulness Checker that verifies quantitative claims against pre-computed ground truth, and evaluate the system on a domain-specific benchmark using four language-model backbones. The suppression property follows from the validation layer under those assumptions, and held across all 276 evaluated agent executions involving 23 queries, four backbones and three runs. Mean exact-plan-match rates ranged from 50.7 to 53.6 per cent, with a between-backbone spread within a single backbone. Because exact plan match penalises valid but non-minimal plans, it is a strict lower bound on task correctness. Given only three runs per backbone, comparisons are descriptive, not evidence of equivalence The on-premise open-weight backbones performed comparably to commercial ones on the measured metrics, supporting confidential deployments.