A universal gateway that aggregates multiple MCP servers into a single interface while providing advanced token optimization, result filtering, and automated summarization. It enables efficient management of large tool catalogs and reduces context usage by up to 95% for major AI clients.
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 unified MCP gateway that reduces context token usage by exposing all services through just two tools (search and execute), supporting built-in services like Asana and Sentry as well as proxied external MCP servers.
Aggregates multiple Model Context Protocol servers into a single gateway to provide unified search, description, and execution of tools. It reduces context limit issues by dynamically fetching specific tool schemas only when needed rather than loading all available tools at once.
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.
Aggregates multiple MCP servers into a single gateway with unified top-level tools, reducing LLM context usage and enabling IDE compatibility by consolidating many tools into fewer interface functions.
