Handwritten text by language learners couples recognition errors with grammatical errors, which makes automatic correction of such text harder than either subproblem alone. Visual Chinese grammatical error correction asks a system to read a handwritten sentence image and to emit both a fully corrected sentence and a list of localized visual edits. We describe a training-free pipeline that makes one call to a vision–language model (VLM) per image and jointly requests a verbatim transcription, a minimal-edit correction, and a typed bounding-box edit list in a single JSON object; a deterministic packer enforces schema and coverage, and a second engine fills only the test images for which the first produces no valid record. A paired 19-image development comparison under a proxy of the official F_0.5 metric finds that two VLMs obtain the same edit true-positive count while one emits nearly twice as many edits; the more conservative engine has higher edit precision on this slice. The system obtains an official score of 0.5905 on the hidden sentence-level test set of the VisCGEC-v2.0 shared task. The result suggests that a single-pass training-free VLM pipeline is viable for this task, and that edit precision is a useful engine-selection signal on the measured development slice.