Enables AI to automatically diagnose bugs by querying logs, tracing call chains, and analyzing code across multiple log platforms like Elasticsearch and Loki.
Enables AI agents and MCP clients to locally debug observability data — traces, observations, sessions, exceptions, route decisions, prompts, datasets, metrics, and annotation queues — through 48 query and management tools. Supports selective tool-group loading to cut token overhead and a read-only mode that disables all write operations.
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.
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 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 unified local API gateway providing caching, rate limiting, and full Model Context Protocol compatibility for AI agent integration. It enables users to aggregate multiple API endpoints into a single gateway with built-in observability and customizable eviction strategies.