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.
A proxy server that wraps existing MCP servers to significantly reduce token consumption by compressing tool descriptions into a two-step interface. It enables users to integrate extensive toolsets without exceeding context limits or incurring high API costs.
Intelligent MCP proxy server that reduces context bloat by serving only the tools your AI actually needs through semantic search and a fixed two-tool surface.
A Model Context Protocol (MCP) server that optimizes token usage by caching data during language model interactions, compatible with any language model and MCP client.