AAIF explains retrieval tool selection for MCP agents

To prevent context bloat and accuracy loss when agents require access to thousands of tools, the author explains a retrieval-based approach where agents query tools in plain language rather than loading an entire static list. The method utilizes embedding and ranking models to return a small set of relevant tools, preserving prompt caching and reducing input tokens while maintaining high task completion rates. However, the author notes that dynamic selection loses its cost advantage when working with fewer than roughly 25 tools or when agents execute excessive searches. Open article →