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.
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 token-efficient MCP server that reduces context window bloat by lazy loading tool descriptions and proxying calls through three simple tools, with a dashboard for managing connections.
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.
A discovery and routing layer for MCP servers that loads tool definitions on demand, reducing token usage by keeping servers out of the context window until needed.