A meta-server that aggregates multiple MCP servers into a single interface, reducing token usage by 98%+ through progressive tool discovery and direct code execution that processes data between tools without consuming context window space.
An MCP server implementation that standardizes how AI applications access tools and context, providing a central hub that manages tool discovery, execution, and context management with a simplified configuration system.
Acts as a proxy for multiple MCP servers, reducing context window usage from 15,000+ tokens to ~500 tokens by dynamically loading servers on-demand and exposing only 3 tools instead of all tool definitions.