Enables agents to use many MCP servers without context bloat by exposing meta-tools (search, load, call, run_code) that reduce token usage via progressive disclosure and result trimming.
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 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.
A generic MCP proxy that filters which tools are exposed from a remote MCP server, reducing context window token usage by only loading the tools you actually need.
A single MCP endpoint for AI agents to browse, inspect, and call tools from multiple upstream MCP servers without loading all schemas upfront, reducing context overhead.
A progressive-disclosure gateway for MCP servers that keeps tool lists small by exposing one top-level tool per server, allowing agents to search, list, inspect, and call underlying tools within a selected domain.