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.
A proxy server that wraps existing MCP servers to significantly reduce token consumption by compressing tool descriptions into a two-step interface. It enables users to integrate extensive toolsets without exceeding context limits or incurring high API costs.
Intelligent MCP proxy server that reduces context bloat by serving only the tools your AI actually needs through semantic search and a fixed two-tool surface.
A smart MCP proxy server that lazily loads relevant MCP tool servers based on project context, keeping AI tool counts within recommended limits. It enables agents to dynamically activate, deactivate, and discover MCP servers to optimize context usage.
An MCP proxy that embeds tool definitions and returns only the top-k semantically relevant tools per query, reducing context usage and routing calls to upstream MCP servers.