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 multiplexing gateway that aggregates multiple MCP servers into a single port, significantly reducing context token usage through a Meta-MCP discovery system. It enables dynamic tool discovery and invocation across various transport protocols including stdio, HTTP, and SSE.
A local MCP gateway that compresses multiple upstream servers into two tools, search and execute, to minimize model context usage. It provides a compact, code-driven interface for discovering and calling tools across various upstream sources on demand.
Enables an LLM to dynamically discover and call tools across multiple MCP servers (file, GitHub, SQL, Python execution) with authentication, rate limiting, and observability, supporting parallel execution and secure deployment.
A unified MCP server providing tools for web search, GitHub analysis, document parsing, browser automation, secure code execution, and forum parsing, enabling LLMs to perform a wide range of external tasks through a single backend.
A production-style MCP gateway that aggregates multiple tool servers into one surface with semantic tool search, RBAC, audit logging, and rate limiting, enabling efficient tool selection for AI agents.
