Large language models hallucinate in 69% to 88% of legal queries, yet current systems only access external tools at inference time, never learning legal research methodology during training. This thesis presents a search-augmented reinforcement learning framework that integrates 18 specialized tools directly into the training loop via a smart Model Context Protocol client. The framework introduces four contributions: multi-tool training with a 9-call episodic constraint, multi-task reward routing using LLM judge ensembles across four legal task types, US jurisdiction compliance gating across all 50 states plus the federal system, and an attention-based interpretability framework for mapping legal reasoning patterns. Implemented with Qwen 2.5 3B and GRPO optimization on 8 A100 GPUs, the system achieves a 10.5 percentage point improvement over baselines on LegalBench (59.6% vs. 49.7%), 97% jurisdictional compliance, and 0.72 Spearman correlation between model attention and expert fact importance rankings.