I'm Samrith, the creator of Hyperterse.
I built *Hyperterse* because I kept running into the same problem when building AI agents: safely connecting databases to LLMs requires writing endless API endpoints, managing boilerplate validation, and building custom integrations for each AI framework.
To build an AI agent that can query your database, you usually have to: - Write REST endpoints for every query - Implement input validation and type checking - Generate OpenAPI documentation separately - Build custom MCP integrations for each AI framework - Worry about exposing SQL or connection strings to clients
We built a runtime server that consolidates this into a single declarative configuration file. You define your queries once, and Hyperterse automatically generates typed REST endpoints, MCP tools, OpenAPI specs, and LLM-friendly documentation.
*HOW IT WORKS:*
Instead of writing API endpoints, you define queries in a simple config file. Hyperterse handles: - *Query Definition*: Write SQL once with typed inputs - *Endpoint Generation*: Each query becomes a REST endpoint (`POST /query/{name}`) - *MCP Integration*: Queries are automatically exposed as MCP tools for AI agents - *Documentation*: OpenAPI 3.0 specs and LLM-readable docs generated automatically - *Security*: SQL and connection strings stay server-side—never exposed to clients
*THE SYNTAX:*
Here's what a typical query looks like. This replaces about 50-100 lines of boilerplate API code:
```yaml adapters: my_db: connector: postgres connection_string: "postgresql://user:pass@localhost:5432/db"
queries: get-user: use: my_db description: "Retrieve a user by email" statement: | SELECT id, name, email, created_at FROM users WHERE email = {{ inputs.email }} inputs: email: type: string description: "User email address" ```
Run `hyperterse run -f config.terse` and you get: - REST endpoint: `POST /query/get-user` with automatic input validation - MCP tool callable by AI agents via JSON-RPC 2.0 - OpenAPI docs at `/docs` - LLM-friendly documentation at `/llms.txt`
*FEATURES:*
- Supports PostgreSQL, MySQL, and Redis (at the moment, more connectors coming) - Hot reloading in dev mode (`hyperterse dev`) - Type-safe input validation (string, int, float, boolean, uuid, datetime) - Self-contained runtime—deploy anywhere - No ORMs or query builders required
*USE CASES:*
- AI agents and LLM tool calling - RAG applications with structured database queries - Rapid API prototyping - Multi-agent systems sharing database access