MCPFast / Tools / Claude token consumption optimizer using local manifests
Reduces Claude token consumption by indexing and summarizing local files for efficient context management.
View on GitHub→This tool, available on GitHub, addresses a critical challenge for developers working with large language models like Claude: managing token consumption. By intelligently indexing and summarizing local files, it enables more efficient context management, significantly reducing the number of tokens required to provide relevant information to the AI. This translates to lower operational costs and faster response times for applications relying on extensive local data.
The Claude token consumption optimizer operates by creating a local manifest of your files. Instead of feeding raw, unsummarized file content directly into Claude's context window, this tool processes and summarizes these files beforehand. When Claude needs information from a specific file, the optimizer retrieves the pre-generated summary, drastically reducing the token count compared to sending the entire file. This is particularly useful for applications that require access to large codebases, documentation, or datasets.
This tool is designed for AI developers , machine learning engineers , and software architects who are building applications that leverage large language models like Claude and need to manage substantial amounts of local data. It is ideal for projects involving:
If you are concerned about the cost and efficiency of providing large amounts of text data to Claude, this optimizer is a valuable addition to your development toolkit.