Large Language Models (LLMs) have shown remarkable capabilities in Tool-Integrated Reasoning (TIR). However, the practical application is often hindered by frequent errors in tool invocations, such as incorrect parameters or malformed formats. Prevailing training paradigms, such as Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), can mitigate these issues but require modification on the base LLM. This lack of modularity necessitates extensive retraining when deploying the system across different base models. To address the limitation, we introduce the Invocation Refiner, a specialized post-processing module designed to enhance the tool-use reliability of base LLMs without directly training on them. The Refiner takes the output from a frozen upstream LLM and the user’s query as input, performing independent reasoning to rectify the invocation. We construct a dedicated training dataset and train this module using an advanced RL algorithm. On a diverse set of tool-use and reasoning benchmarks, our Refiner improves task completion rates and invocation accuracy over the raw outputs of various upstream LLMs. This highlights our Refiner as a plug-and-play solution for improving the operational reliability of LLM-based agents. We release our code to facilitate future research.