Biological vision integrates cues according to their reliability and context and uses coarse contextual information to form initial hypotheses before directing attention to local evidence. Inspired by these principles, we propose ADCCR (Adaptive Description Composition and Coarse-to-Fine Refinement), a language-guided framework for flexible human keypoint localization. A multi-granularity bank describes each keypoint by its name, anatomical definition, inter-joint relation, and visual appearance. A reliability-aware composer predicts sample-specific gates from the person image, target identity, and candidate cues, replacing predefined sampling probabilities. Counterfactual cue-consistency supervision suppresses inconsistent cues, while a sparse cue-budget regularizer reduces redundancy. The selected description guides a multimodal large language model to estimate a coarse coordinate from the whole-person image. A category-aware crop of the resized person image then focuses text-conditioned heatmap refinement on the candidate region. ADCCR thus provides a functional abstraction of reliability-aware cue integration and selective global-to-local inspection. Trained on COCO, ADCCR achieves 80.1 AP on the validation set, outperforming PoseLLM by 2.5 points. Results on MPII and Human-Art further demonstrate strong cross-dataset generalization.