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.
Universal MCP server for executing TypeScript and Python code with progressive disclosure, reducing token usage by 98% by enabling on-demand access to all other MCP tools through code execution rather than loading tool definitions directly.
An MCP server that enables AI agents to execute sandboxed JavaScript and TypeScript code instead of calling individual tools directly. It significantly reduces token usage by allowing agents to filter, aggregate, and transform data locally before returning results.
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.
A self-hosted MCP server that provides a single execute_code tool, enabling agents to write TypeScript to call multiple REST APIs via fetch() with transparent credential injection, reducing token usage by keeping intermediate results in the sandbox.
