Database Intelligence Layer

@terno-ai

About Database Intelligence Layer

TernoDBI is a database intelligence layer designed for Security and Accuracy, bridging the gap between AI Agents and Enterprise Data. It acts as a powerful standalone Model Context Protocol (MCP) server, or it can be directly embedded into your existing Django projects. Either wa

#security #accuracy

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "ternodbi-query": {
      "command": "uvx",
      "args": [
        "--from",
        "terno-dbi",
        "dbi-mcp",
        "query"
      ],
      "env": {
        "TERNODBI_API_URL": "http://127.0.0.1:8376",
        "TERNODBI_API_KEY": "dbi_query_YOUR_TOKEN_HERE"
      }
    },
    "ternodbi-admin": {
      "command": "uvx",
      "args": [
        "--from",
        "terno-dbi",
        "dbi-mcp",
        "admin"
      ],
      "env": {
        "TERNODBI_API_URL": "http://127.0.0.1:8376",
        "TERNODBI_API_KEY": "dbi_admin_YOUR_TOKEN_HERE"
      }
    }
  }
}

Tools

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Overview

What is Database Intelligence Layer?

TernoDBI (the Database Intelligence Layer) is an MCP server that bridges AI agents and enterprise databases. It provides a secure, unified API for warehouse-scale databases while enforcing row-level security, schema enrichment, and multi-database support.

How to use Database Intelligence Layer?

Install via pip install terno-dbi, run ternodbi start, configure datasources in the admin panel, generate an access token with the CLI (ternodbi manage issue_token), then connect MCP clients like Claude Desktop using the provided JSON configuration. The server runs on 127.0.0.1:8376.

Key features of Database Intelligence Layer

Multi-database support: Postgres, MySQL, Snowflake, BigQuery, Databricks, Oracle, SQLite
Split MCP architecture: Query Server (read-only) and Admin Server (write/management)
Enterprise-grade security: Row-Level Security, Privacy-by-Default, SQLShield AST validation
LLM-ready schema enrichment with semantic metadata and statistical profiling
High-performance cursor-based pagination (HMAC) with ~28x speedup
Direct integration into existing Django projects as an embedded app

Use cases of Database Intelligence Layer

Secure querying of enterprise databases by AI assistants with granular access control
Enabling natural-language database exploration via Claude Desktop
Embedding into custom AI agent workflows (LangChain, LlamaIndex) using REST APIs
Adding intelligence to existing Django web applications through direct app integration

FAQ from Database Intelligence Layer

What databases does Database Intelligence Layer support?

It supports Postgres, MySQL, Snowflake, BigQuery, Databricks, Oracle, and SQLite.

How does Database Intelligence Layer ensure security?

It provides Row-Level Security (SQL-based filters), privacy-by-default hiding of sensitive columns and tables, and SQLShield AST-based validation to prevent SQL injection and destructive operations.

Can Database Intelligence Layer be integrated into existing Django projects?

Yes, add terno-dbi to your environment, include 'terno_dbi.core' in INSTALLED_APPS, mount its URLs, and run migrations. Then use its models and services programmatically.

What are the runtime requirements?

Python 3.10+ and Django 4.2+ are required. The server runs locally on 127.0.0.1:8376.

How do I generate access tokens?

Use the CLI: ternodbi manage issue_token --name "Agent Name" --type query (or admin for admin tokens). Tokens can be scoped to a specific datasource and can have expiration dates.

Frequently asked questions

What databases does Database Intelligence Layer support?

It supports Postgres, MySQL, Snowflake, BigQuery, Databricks, Oracle, and SQLite.

How does Database Intelligence Layer ensure security?

It provides Row-Level Security (SQL-based filters), privacy-by-default hiding of sensitive columns and tables, and SQLShield AST-based validation to prevent SQL injection and destructive operations.

Can Database Intelligence Layer be integrated into existing Django projects?

Yes, add `terno-dbi` to your environment, include `'terno_dbi.core'` in `INSTALLED_APPS`, mount its URLs, and run migrations. Then use its models and services programmatically.

What are the runtime requirements?

Python 3.10+ and Django 4.2+ are required. The server runs locally on `127.0.0.1:8376`.

How do I generate access tokens?

Use the CLI: `ternodbi manage issue_token --name "Agent Name" --type query` (or `admin` for admin tokens). Tokens can be scoped to a specific datasource and can have expiration dates.

Comments

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