Elegant Solution to Prompt Bloat: Semantic Retrieval of Tools for Efficient LLM Inference
The article explains how the limited context window of large language models causes prompt bloat when many tool descriptions are embedded, and presents the RAG‑MCP architecture that stores tool metadata in a vector database, uses semantic retrieval to select only the most relevant tools, dramatically shortens prompts, and improves inference speed and tool‑call accuracy.
