Graph Neural Networks (GNNs) increasingly follow the ''pre-training, adaptation'' paradigm, where a GNN is pre-trained on large-scale graphs and then adapted to downstream tasks. Graph prompting adapts to the frozen encoder by modifying the input graph structure, rather than fine-tuning the model parameters. However, existing graph prompting methods often rely on probabilistic rewiring and auxiliary regularizers to control sparsity, which makes the prompting process sensitive to hyperparameters and can introduce instability in few-shot settings. To address the issue, we propose ProtoCalib, a lightweight local graph prompt for few-shot adaptation of frozen GNNs. ProtoCalib uses prototypes from the support set to score candidate edges for each anchor node. It then calibrates these scores with a per-node edge budget, which keeps the prompted graph sparse and removes the need for extra sparsity or entropy losses. We further adopt a deterministic construction of the prompted adjacency to reduce sampling noise at inference time. Extensive experiments on five graph datasets under four pre-training strategies demonstrate that our proposed ProtoCalib outshines baselines on multiple node classification datasets.