Cursor has introduces a new approach to minimize the context size of requests sent to large language models. Called dynamic context discovery, this method moves away from including large amounts of st

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Cursor introduces dynamic context discovery, a technique that reduces LLM token usage by 46.9% by allowing AI agents to retrieve context on-demand rather than loading everything upfront. The approach uses five techniques centered around file-based interfaces: writing large outputs to files, preserving full history, storing domain-specific capabilities, lazy-loading MCP tool details, and syncing terminal output to the filesystem. This reduces costs, improves response speed, and prevents irrelevant information from confusing the agent.

AI-Powered Code Editor Cursor Introduces Dynamic Context Discovery to Improve Token-Efficiency