MCP servers can consume a significant portion of the context window, with tool definitions using up to 55,000 tokens before processing user messages. Each MCP tool requires between 550 and 1,400 tokens for its name, description, and schema, impacting the available token limit for AI tasks.

Fast KV Compaction via Attention Matching addresses the limitations of key-value cache size in scaling language models for long contexts. It proposes a method that improves context management without the lossy effects of traditional summarization techniques.