Aggregates tools from multiple upstream MCP servers and exposes them through 4 meta-tools, enabling LLMs to discover and use hundreds of tools without loading all schemas upfront.
A local MCP gateway that compresses multiple upstream servers into two tools, search and execute, to minimize model context usage. It provides a compact, code-driven interface for discovering and calling tools across various upstream sources on demand.
A drop-in MCP proxy that aggregates multiple backend servers into two meta-tools for efficient tool discovery and execution. It enables AI clients to access hundreds of tools while minimizing context window usage through searchable indexing.
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.
A Model Context Protocol server that wraps multiple backend servers for token-efficient tool discovery via lazy loading. It enables AI models to browse available servers and fetch specific tool schemas on-demand, significantly reducing initial context overhead.