Show HN: SpreadsheetMCP – Token-efficient Excel tools for LLM agents (Rust) (github.com)

A new tool called SpreadsheetMCP has been unveiled, designed to optimize Excel spreadsheet analysis and editing for large language model (LLM) agents. This Rust-based server provides a token-efficient interface that allows agents to interact with spreadsheets without needing to load entire files into context, making it a significant advancement for the AI/ML community. Instead of dumping vast amounts of data, SpreadsheetMCP focuses on targeted access, enabling users to discover, profile, and extract specific data regions while minimizing token usage. The server supports various Excel formats (.xlsx, .xlsm, .xls, .xlsb) and offers a rich suite of over a dozen tools for different tasks, such as structured reads, formula analysis, and data profiling. A notable feature is the lazy computation of sheet metrics and on-demand region detection, which enhances performance by caching frequently accessed data characteristics. Additionally, it provides "what-if" analysis capabilities, enabling users to manipulate data and recalculate results efficiently, further streamlining data processing for AI applications. This innovative approach contributes to a more efficient and effective integration of LLMs with spreadsheet data analysis tasks, ultimately enhancing productivity for data-driven AI applications.