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Feb 13, 2026

Building an Enterprise MCP Server on Oracle Autonomous AI Database (ADW 26ai)

Artificial Intelligence inside Oracle Autonomous Database is no longer experimental — it’s becoming operational.

In this article, we walk through Autonomous AI Database MCP Server ( available in Autonomous database ADW26ai) , a built-in feature designed to bridge the gap between AI models and database resources using the Model Context Protocol (MCP). This standardized interface allows developers to connect AI agents and applications to the database without building custom integrations, simplifying how models access data, tools, and state.

But enterprise AI requires more than just natural language to SQL.

It requires:

  • Governance
  • Deterministic business logic
  • Observability
  • Rate limiting
  • Security controls

By leveraging the Oracle Autonomous AI Database, the architecture ensures deterministic logic through predefined semantic tools and strict database-level guardrails. This system enables users to receive instant business intelligence from natural language queries while maintaining rigorous audit trails and rate limiting. Ultimately, how to transform experimental AI into a production-ready platform that provides reliable insights across sales, finance, and operations.

At Bizinsight Consulting, we recently built a production-ready MCP architecture on Oracle ADW 26ai that enables:

  • Natural language → governed tool execution
  • Fully logged and rate-limited SQL access
  • Semantic business intelligence tools
  • Secure, controlled execution (no unsafe SQL)

In this article, we walk through how Bizinsight Consulting designed and implemented a production-ready Model Context Protocol (MCP) server architecture using Oracle Autonomous AI Database (ADW 26ai).

 The Enterprise Use Case

Imagine executives asking:

  • “Show top 5 customers by revenue.”
  • “Which orders are not invoiced?”
  • “What percentage of orders are fully fulfilled?”

Instead of dashboards or manual SQL, these questions are answered through an AI-powered assistant — governed, logged, and secure.That’s where MCP comes in.


The 3-Layer Architecture

Our implementation follows a clean enterprise architecture pattern:

1.   Presentation Layer

a. VS Code or enterprise AI client interface
b.Users submit natural language prompts.

2.  Orchestration Layer

a. Oracle AI Agent (DB-native MCP)
b.LLM performs reasoning and selects appropriate tools.

3.  Execution Layer

a. Oracle Autonomous Data Warehouse (ADW 26ai)
b.PL/SQL semantic tools execute governed business logic.

This separation ensures intelligence without sacrificing control.

What We Built Inside ADW

Instead of letting the LLM freely generate SQL, we implemented controlled tools:

  • TOP_CUSTOMERS_BY_ORDER_COUNT
  • TOP_CUSTOMERS_BY_REVENUE
  • ORDER_INVOICE_RECON
  • SHIPMENT_FULFILLMENT_SUMMARY
  • EXECUTE_SQL_SELECT (guarded)

Each tool:

  • Executes deterministic logic
  • Returns structured JSON
  • Logs execution lifecycle
  • Enforces rate limits
  • Blocks unsafe operations

Governance & Security Controls

Enterprise AI cannot rely on trust alone.

We implemented:

✅ SELECT-Only Guardrails

DML/DDL keywords blocked




✅ Rate Limiting

Per-user query limits enforced inside ADW






✅ Full Execution Logging

START → SUCCESS → ERROR → BLOCKED lifecycle tracking

✅ Pagination Caps

Prevents large-scale data extraction

This transforms MCP from a demo into an enterprise-ready platform.


Business Capabilities Delivered

With this architecture, organizations gain:

Sales Intelligence

  • Top customers by revenue
  • Order distribution trends

Finance Intelligence

  • Invoice status summaries
  • Revenue concentration analysis

Operations Intelligence

  • Shipment fulfillment %
  • Backorder visibility
  • Order-to-invoice reconciliation


Why Not Just Use Select AI?

Select AI is powerful for natural language analytics

But MCP provides:

  • Deterministic KPI definitions
  • Controlled business logic
  • Tool-level governance
  • Production-ready architecture



  • Select AI accelerates analytics.

  • MCP operationalizes enterprise AI.

 Lessons Learned

Designing AI inside Oracle requires:

  1. Governance from day one
  2. Logging as a first-class feature
  3. Tool-based architecture over free-form SQL
  4. Clear separation of orchestration and execution
  5. Production thinking — not experimentation

The Result

We built a secure, governed AI assistant powered entirely by:

  • Oracle Autonomous AI Database
  • DBMS_CLOUD_AI_AGENT tool framework
  • PL/SQL semantic tools
  • Enterprise-grade observability


This is not just AI — it’s operational intelligence.

Thinking About AI in Your Oracle Environment?

At Bizinsight Consulting, we help enterprises design:

  • Select AI strategies
  • MCP-based AI assistants
  • Secure integration architectures
  • Production-ready analytics platforms
👉  Contact Bizinsight Consulting
Email us : inquiry@bizinsightinc.com
https://www.bizinsightinc.com/