MCP server that reduces AI agent token usage by up to 90% through intelligent context compression. Enables efficient code exploration, multi-file refactoring, and debugging by providing tools for smart reading, searching, and managing code context.
An MCP server giving coding agents context-window-aware code search and safe, atomic multi-file edits — built to cut token usage on large codebases without sacrificing correctness.
Reduces token consumption by over 80% through intelligent file caching, returning only diffs for modified files and suppressing unchanged content. It features a suite of 12 tools for semantic search, batch reading, and efficient file editing to optimize LLM interactions with large codebases.
Agent-optimized MCP server that replaces built-in file, search, exec, and git tools with compact, structured JSON equivalents. Benchmarked 20–45% token savings for AI coding agents.
An MCP server that delegates filesystem operations to a specialized React agent, reducing context usage and improving accuracy for AI applications like Claude Code.
MCP server that combines Read+Edit file operations into single tool calls. 80-95% fewer tool calls formulti-file refactoring across Claude, Cursor, Windsurf, and more.
