A lightweight and fast MCP server that enables AI agents to efficiently discover and execute tools through progressive disclosure, minimizing context consumption while supporting safe code execution in external environments.
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.
A production-grade MCP server designed for multi-tenant, authenticated, and observable AI agent systems, enabling secure tool execution across heterogeneous data sources.
Enables building safe, programmable Code Mode MCP servers from upstream tool sources, with model-authored TypeScript running in a sandbox and tool calls routed to trusted host-side providers.
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.
Agent-optimized MCP server that replaces built-in file, search, exec, and git tools with compact, structured JSON equivalents. Benchmarked 20–45% token savings for AI coding agents.
