Hyperterse is a full MCP framework, not just a data layer.
Instead of stitching APIs, SDKs, and agents manually, you declaratively define tools and Hyperterse compiles them into production-ready MCP servers. It standardizes how agents discover, reason about, and execute capabilities and turning your backend into a native agent runtime with built-in auth, observability, and multi-system orchestration.
Hyperterse treats data access as declarative infrastructure, rather than existing data tools that rely on insecure Text-to-SQL or tedious manual APIs.
Define queries once, and we auto-generate secure Model Context Protocol (MCP) tools and REST endpoints.
Standout features include "Security-by-Abstraction" (agents never see raw SQL), automatic input validation, and real-time generation of LLM-friendly documentation.
It bridges the "Data Access Gap" for your Postgres, MySQL, and Redis data.
The "10-to-1" rule says we spend 10x more time reading code than writing it.
Yet, when connecting AI agents to databases, we are seeing developers drowning in manual boilerplate writing endpoints, validation logic, and maintaining llms.txt files just to expose a simple query.
