A new analysis suggests that the perceived high cost of using tool schemas with large language models like Claude is often overstated, with actual schema costs being significantly lower than community estimates. The primary driver of context window consumption is not the initial schema definitions, but rather the accumulating responses from tool calls throughout a session. These responses, such as page snapshots or data query results, can quickly consume a large portion of the context window, making them a more critical area for optimization than the initial schema loading. AI
IMPACT Highlights that optimizing tool response handling, rather than just schema loading, is key to managing LLM context window costs.
RANK_REASON The article provides an analysis and measurement of LLM context window costs, offering an opinion on where the real cost lies.
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →
