Hyperterse

Product Introduction

1. Definition: Hyperterse is a declarative data infrastructure runtime that automates secure API and tool generation for databases. It operates as a high-performance gateway between production databases (Postgres, MySQL, Redis) and downstream consumers like AI agents or applications.
2. Core Value Proposition: Hyperterse eliminates the "Data Access Gap" by replacing insecure Text-to-SQL or manual API development with automated, security-first query execution. Its primary value lies in zero-boilerplate data exposure—transforming raw SQL into validated REST endpoints and AI-ready tools without code.

Main Features

1. Declarative Query Configuration:
   Define SQL queries and parameters in a YAML-like config.terse file. Hyperterse’s runtime engine parses this configuration to auto-generate endpoints at POST /query/{query-name}. It enforces strict input validation using inferred types (e.g., email: string! for non-nullable strings) and executes parameterized SQL to prevent injections.
2. Security-by-Abstraction:
   Implements a zero-trust data access model where agents interact solely with generated endpoints—never raw SQL, schemas, or credentials. Connection strings and query logic are encapsulated server-side, while runtime errors are sanitized to avoid data leaks. Uses TLS encryption for all client-server communications.
3. Multi-Format Documentation Automation:
   Generates OpenAPI 3.0 specs (via GET /docs), LLM-readable Markdown (GET /llms.txt), and MCP (Model Context Protocol) tool definitions for AI agents. Documentation stays synced with configuration changes, enabling real-time discoverability of endpoints and their JSON-RPC 2.0 call signatures.
4. Database Agnosticism:
   Supports PostgreSQL, MySQL, and Redis via dedicated adapters. Queries use native SQL syntax per database, while Hyperterse standardizes input validation, output serialization (JSON), and connection pooling across engines.
5. Portable Runtime:
   Ships as a self-contained binary (installable via cURL, NPM, Bun, or Homebrew) with no external dependencies. Deploys identically from local development to production, reducing environment drift.

Problems Solved

1. Pain Point: Manual API development for database access introduces security risks (e.g., SQL injection), development latency (weeks per endpoint), and documentation drift.
2. Target Audience:
   • AI/ML Engineers building agentic systems requiring real-time data.
   • Backend Developers creating microservices for Postgres/MySQL.
   • DevOps Teams managing secure data gateways.
3. Use Cases:
   • Exposing CRM data to ChatGPT plugins via MCP tools.
   • Rapid prototyping of analytics APIs without writing Express.js routes.
   • Securing LLM database access by replacing Text-to-SQL with validated endpoints.

Unique Advantages

1. Differentiation vs. Competitors:
   Unlike ORMs (e.g., Prisma) that abstract SQL, Hyperterse embraces raw SQL while automating security and delivery. Contrasted with Hasura (GraphQL), it prioritizes AI/LLM integrations via MCP and eliminates GraphQL’s overfetching risks.
2. Key Innovation:
   Model Context Protocol (MCP) Tool Generation—Hyperterse dynamically packages queries into JSON-RPC 2.0-compliant tools agents invoke natively. Combined with "Security-by-Abstraction", this prevents prompt-based SQL leaks common in LangChain or LlamaIndex workflows.

Frequently Asked Questions (FAQ)

1. How does Hyperterse improve AI agent security?
   Hyperterse isolates agents from databases by auto-generating MCP tools—agents call predefined endpoints without accessing SQL or credentials, eliminating Text-to-SQL injection risks.
2. Can Hyperterse handle complex, multi-database queries?
   Yes, Hyperterse supports cross-database workflows by allowing separate adapters per query. For example, join Redis-cached data with Postgres via independent connectors in one config.terse file.
3. Is Hyperterse suitable for high-traffic production APIs?
   Absolutely. Its runtime uses connection pooling and async I/O, handling thousands of requests/sec. Input validation and parameterized SQL ensure reliability under load.
4. How does documentation stay updated with configuration changes?
   Hyperterse regenerates OpenAPI specs, MCP definitions, and llms.txt in real-time on server restart. No manual syncing is needed.
5. What’s required to migrate from existing REST APIs to Hyperterse?
   Migrate SQL queries into config.terse, define input parameters, and deploy the runtime. Existing API consumers switch to Hyperterse’s auto-generated endpoints (e.g., POST /query/user-by-email).