Cursor Shares Harness Tuning Prompt to Cut Agent Token Overhead
Cursor has shared a prompt for improving the token efficiency of AI agent harnesses. One round of prompt trimming, tool offloading, and cache layout changes cut one production team's overall token cost by about 7% with no loss in quality. Offloading non-core tools cut tool-description tokens by 60%, and making Model Context Protocol schemas discoverable via grep or jq cut total session tokens by 46.9% in sessions that used them.
Why it matters
Cursor has shared a prompt for improving the token efficiency of AI agent harnesses. One round of prompt trimming, tool offloading, and cache layout changes cut one production team's overall token cost by about 7% with no loss in quality. Offloading non-core tools cut tool-description tokens by 60%, and making Model Context Protocol schemas discoverable via grep or jq cut total session tokens by 46.9% in sessions that used them.