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.
A token-efficient MCP server that reduces context window bloat by lazy loading tool descriptions and proxying calls through three simple tools, with a dashboard for managing connections.
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.
An MCP server that indexes codebases into a local graph and provides on-demand context retrieval for AI coding agents, reducing token usage by tracking session history and delivering only relevant code subgraphs.
