FullScope-MCP – Cut agent token usage by 60% without lossy summarization (github.com)

FullScope-MCP has introduced a groundbreaking context optimization layer for large language models (LLMs) that significantly cuts agent token usage by 60% while preserving executable logic. This development addresses a critical challenge in AI and machine learning—optimizing LLM interactions with real-world codebases. Traditional methods, such as reading full files or using summarization, often lead to trade-offs that compromise either context or accuracy. FullScope eliminates these dilemmas by allowing LLMs to reason over entire files and larger project segments, enhancing the quality of outputs without necessitating code abstraction or loss of detail. The technology utilizes a unique approach by structurally compressing code, enabling models to read nearly twice as much in context mode and up to five times more in skeleton mode. This entails three distinct visibility levels for code: full logic, structural signatures, and function-level detail, each tailored for specific tasks like refactoring, debugging, or onboarding to unfamiliar codebases. Benchmarked across various programming languages and file types, FullScope has demonstrated impressive token savings while maintaining integrity—ensuring that the original code remains untouched. This capability positions FullScope as a vital tool for developers, enhancing the efficiency and accuracy of AI-driven coding tasks without the risk of altering source files.