An MCP aggregator that consolidates multiple MCP servers behind a single interface with just 3 tools (search, get details, execute), reducing context pollution for AI agents by avoiding direct exposure of numerous tool schemas.
A browse-first MCP middleware that provides an LLM-friendly catalogue for discovering and invoking tools across multiple upstream MCP servers, reducing context overhead.
A drop-in MCP proxy that aggregates multiple backend servers into two meta-tools for efficient tool discovery and execution. It enables AI clients to access hundreds of tools while minimizing context window usage through searchable indexing.
Enterprise-grade dynamic MCP proxy that eliminates token bloat by lazy-loading tool schemas based on semantic intent, enabling efficient orchestration of multiple backend tools from a single endpoint.
A context-aware MCP proxy that reduces token usage by exposing only 3 tools (mcp_search, mcp_call, mcp_schema) to LLMs, with on-demand tool loading and BM25 search.
