Small language models (1.5B-4B parameters) offer low-cost, low-latency tool use but often produce plausible plans that fail during execution due to unavailable tools, missing arguments, or broken dependencies. We introduce EvoFlux, an inference-time search method that repairs tool plans through an evolutionary loop over structured graphs of tool calls and dependencies. EvoFlux executes candidate plans against live tool servers and uses execution errors to guide targeted edits, including replacing tools, correcting parameters, inserting missing steps, and removing redundant ones. On held-out MCP-Bench tasks spanning 28 live servers and 250 tools, EvoFlux improves execution success from roughly 3% to 17-24% across four small planners. SFT and SFT+DPO trained on the same 177 demonstrations and 624 preference pairs match or underperform the zero-shot baseline, while ReAct reaches higher peaks on stronger planners at 4-20x greater planner-token cost per solved task. Execution-guided inference-time repair reliably improves small tool-using agents when supervision is limited.

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