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 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.
Token-optimized MCP server that reduces context window usage by 59.5% by grouping 12 tools into 5 semantic operations, preserving all original functionality for AI assistants.
An MCP server that reduces token usage by lazily loading skills and tools only when needed, and routing repetitive subtasks to ML backends instead of the LLM.
A local MCP server that improves token efficiency for Cursor's agent by providing cheaper tools like search_code, outline_file, read_snippet, project_map, and handoff, along with rules that steer the agent to use them and reduce excessive context usage.
