Table of Contents
Hyperterse
Hyperterse treats data access as declarative infrastructure, positioning itself against existing data tools that rely on insecure Text-to-SQL or cumbersome manual API creation. Developers define queries once, and the platform auto-generates secure Model Context Protocol (MCP) tools and REST endpoints. Standout features include “Security-by-Abstraction” (ensuring agents never see raw SQL), automatic input validation, and real-time generation of LLM-friendly documentation. It bridges the “Data Access Gap” for Postgres, MySQL, and Redis data.
Features
- Declarative Infrastructure: Define data access patterns, permissions, and validation rules once using a unified syntax, automatically generating necessary downstream tools.
- Security-by-Abstraction: Prevents AI agents from viewing or executing raw SQL by exposing only high-level semantic tools, effectively neutralizing SQL injection risks.
- Auto-Generated Endpoints: Instantly creates type-safe REST APIs with OpenAPI specifications and MCP tool definitions from your schema.
- Universal Database Support: Provides a unified query language that bridges multiple databases including PostgreSQL, MySQL, and Redis.
- Real-Time Documentation: Automatically generates and updates LLM-friendly documentation, ensuring AI assistants always have accurate context about available data tools.
- MCP Server Compatibility: Native integration with the Model Context Protocol, making data immediately accessible to clients like Claude, Cursor, and other AI assistants.
How It Works
Developers define database queries using Hyperterse’s declarative syntax, which specifies the data access patterns, required permissions, and validation rules. The platform then compiles these definitions into type-safe REST endpoints and MCP tool schemas. When an AI agent or application requests data, Hyperterse acts as a secure intermediary. It validates the input, executes the pre-defined parameterized query, and returns structured results. This abstraction layer ensures that raw database access and SQL syntax are never exposed to the LLM, maintaining strict security and performance standards.
Use Cases
- Secure AI Agents: enabling LLMs to retrieve business data without risking database integrity or leaking schema details.
- RAG Systems: building retrieval-augmented generation pipelines that require structured, deterministic database queries rather than fuzzy vector search.
- Enterprise Compliance: applications requiring strict SQL injection prevention and audit trails for all AI-initiated data access.
- Rapid API Development: generating production-ready APIs from database schemas without writing boilerplate backend code.
- Unified Data Layer: serving data from disparate sources (Redis cache, SQL primary) to AI tools like Claude and Cursor through a single interface.
Pros & Cons
- Pros: Security-by-abstraction eliminates SQL injection risks; Declarative “Infrastructure-as-Code” approach simplifies maintenance; Native MCP support for modern AI clients; Auto-generates both REST and MCP interfaces; Unified support for SQL and NoSQL stores.
- Cons: Relatively new platform with a smaller community than established ORMs; Requires learning a specific declarative syntax; Abstraction layer may restrict highly complex, non-standard query optimizations; Pricing transparency is limited for enterprise tiers.
Pricing
Pricing is not publicly disclosed on the main landing page, suggesting a sales-led enterprise model or a usage-based tier that requires account creation to view. The open-source components on GitHub suggest a potential self-hosted option for developers, while the managed service likely follows a tiered subscription model based on API calls or connected data sources.
How Does It Compare?
Hyperterse differentiates itself by focusing specifically on the Model Context Protocol (MCP) and security-by-abstraction, whereas traditional tools focus on general API generation or ORM capabilities.
- Google MCP Toolbox (formerly Gen AI Toolbox): Google’s open-source offering provides similar MCP connectivity for databases like AlloyDB and Cloud SQL. Comparison: Hyperterse offers a more cloud-agnostic, managed experience with a unified syntax, whereas Google’s toolbox is more “do-it-yourself” and optimized for Google Cloud infrastructure.
- Supabase: A full backend-as-a-service providing a managed Postgres instance with auto-generated APIs. Comparison: Supabase is a complete hosting platform, while Hyperterse is an overlay layer. Hyperterse is better suited for teams who already have a database and need a secure AI access layer without migrating their data.
- Hasura: Famous for instantly generating GraphQL and REST APIs from databases. Comparison: Hasura excels at complex permissioning and GraphQL, but Hyperterse is specifically optimized for LLM tool calling (MCP) and preventing “hallucinated SQL,” which is less of a focus for Hasura.
- Prisma: A TypeScript ORM for building backends. Comparison: Prisma requires writing manual code to create API endpoints. Hyperterse is declarative—you define the what, and it generates the endpoints and AI tools automatically, saving significant development time for AI features.
- Text-to-SQL Tools: Solutions that let LLMs write SQL directly. Comparison: Hyperterse explicitly rejects this model due to security risks. Instead of letting the AI write SQL, Hyperterse forces the AI to use pre-defined, safe tools, offering superior security for enterprise use cases.
Final Thoughts
Hyperterse addresses a critical bottleneck in the 2026 AI stack: securely connecting reasoning models to deterministic business data. By betting heavily on the Model Context Protocol (MCP) and rejecting the insecure “text-to-SQL” paradigm, it offers a mature solution for enterprises rushing to build agents that are both useful and safe. While it requires learning a new syntax, the payoff—instant, secure tools for Claude and Cursor—makes it a compelling piece of infrastructure for the agentic web.