This paper proposes a method for transforming a general-purpose large language model agent into a domain-specific expert. This system achieves specialization by systematically generating, abstracting, and curating reusable Model Context Protocol (MCP) tools from successful task executions, which are then stored in an MCP Box. At inference time, a Retrieval-Augmented Generation (RAG) mechanism selects the most contextually relevant tools from the box, thereby enhancing the agent's problem-solving accuracy and computational efficiency. Experimental results on challenging benchmarks like GAIA, PathVQA, and Humanity’s Last Exam demonstrate that ALITA-G attains new state-of-the-art performance while simultaneously achieving a significant reduction in average token consumption compared to generalist baselines. The overall process converts transient solutions into reusable competence, offering a new paradigm for automated agent generation focused on capability expansion.