Skip to main content

​
Understanding the Model Context Protocol (MCP)

The Model Context Protocol (MCP) is an open standard that enables AI agents to securely access and interact with your data and systems. For IBM i professionals, MCP provides a bridge between modern AI applications and traditional enterprise systems, maintaining the security and reliability standards you expect.
Think of MCP as a standardized API that AI agents understand natively. Instead of building custom integrations for each AI tool, MCP provides a universal interface that works with any MCP-compatible agent.

​
What Problem Does MCP Solve?

Traditional AI applications face significant challenges when working with enterprise data:

Data Silos

Enterprise data locked in systems that AI can’t easily access

Security Concerns

Exposing sensitive data through broad API access or data dumps

Integration Complexity

Building custom connectors for every AI tool and data source

Maintenance Overhead

Managing multiple integration points and authentication methods
MCP solves these problems by providing a standardized, secure, and controlled way for AI agents to access exactly the data they need, when they need it.

​
MCP in IBM i Environments

For IBM i systems, MCP is particularly valuable because it:
MCP integrates with IBM i’s comprehensive security system, respecting user profiles, object authorities, and audit requirements. AI agents can only access data that the authenticated user has permission to see.
User JSMITH → MCP Server → DB2i (as JSMITH)
✓ Inherits JSMITH's authorities
✓ Audit trail maintained
✓ Object-level security enforced
Connect cutting-edge AI agents to decades of business-critical data stored in DB2 for i, without compromising on security or performance.Example Use Cases:
  • AI-powered business intelligence and reporting
  • Automated system monitoring and alerting
  • Intelligent data analysis and trend identification
  • Natural language queries against business data
MCP implementations can include comprehensive logging, audit trails, and compliance features required in regulated industries.Enterprise Features:
  • OpenTelemetry integration for observability
  • Structured audit logging for compliance
  • Rate limiting and resource protection
  • Multi-environment configuration management

​
How MCP Works: A High-Level View

MCP creates a three-party interaction between your data, the MCP server, and AI agents:

​
The MCP Conversation Flow

Here’s what happens when an AI agent needs information from your IBM i system:
  • 1. Agent Request
  • 2. Server Processing
  • 3. Structured Response
The AI agent identifies a need for data and makes a request through MCP:
{
  "method": "tools/call",
  "params": {
    "name": "query_active_jobs",
    "arguments": {
      "user_filter": "ACCOUNTING",
      "max_rows": 10
    }
  }
}

​
MCP Components and Concepts

Understanding these key concepts will help you work effectively with MCP:

​
Tools

Tools are discrete operations that AI agents can perform through MCP. In the IBM i context, tools typically:

Query Data

Execute SQL queries against DB2 for i tables and views
tools:
  system_status:
    description: "Get current system performance metrics"
    statement: "SELECT * FROM qsys2.system_status_info"

System Operations

Perform system administration tasks and monitoring
tools:
  check_subsystems:
    description: "List active subsystem status"
    statement: "SELECT * FROM qsys2.subsystem_info WHERE status = 'ACTIVE'"

Business Logic

Execute stored procedures and business functions
tools:
  calculate_inventory:
    description: "Run inventory calculation procedure"
    statement: "CALL mylib.calc_inventory(:product_line)"

Report Generation

Generate formatted reports and summaries
tools:
  monthly_sales:
    description: "Generate monthly sales summary"
    statement: "SELECT * FROM sales_summary WHERE month = :target_month"

​
Resources

Resources represent data that can be retrieved and analyzed. Unlike tools that perform actions, resources provide static or semi-static content:
  • Configuration files and documentation
  • Reference data and lookup tables
  • System catalogs and metadata
  • Log files and audit trails

​
Capabilities

MCP servers declare their capabilities to inform clients about supported features: 
{
  "capabilities": {
    "resources": { "listChanged": true },
    "tools": { "listChanged": true },
    "logging": {}
  }
}

​
IBM i MCP Server Capabilities

The IBM i MCP Server provides enterprise-grade capabilities specifically designed for IBM i environments:

YAML-Defined SQL Tools

Create powerful SQL operations using simple YAML configurations without writing TypeScript code.

IBM i Authentication

Integrated authentication supporting IBM i user profiles and HTTP auth with RSA encryption.

Authority Integration

Respects IBM i object authorities, special authorities, and security requirements.

Audit & Compliance

Comprehensive audit logging for regulatory compliance and security monitoring.

Production Ready

Enterprise features including OpenTelemetry, error handling, and operational monitoring.

Multi-Transport

Supports both STDIO (development) and HTTP (production) transport modes.

​
Security and Trust Model

MCP implements a security model based on explicit consent and minimal access:

​
Key Security Principles

AI agents can only access tools and data explicitly made available through the MCP server configuration. There’s no blanket access to your systems.
# Only specific tools are exposed
toolsets:
  read_only_reports:
    tools: [sales_summary, inventory_levels]
    # No access to payroll, financial, or system administration tools
All access requires valid authentication. The IBM i MCP Server supports multiple auth modes including IBM i HTTP authentication with encrypted credential exchange.
# User must authenticate before any tool access
MCP_AUTH_MODE=ibmi
IBMI_HTTP_AUTH_ENABLED=true
All operations are logged with full context, including user identity, requested operations, and results. Perfect for compliance and security monitoring.
{
  "timestamp": "2024-01-15T10:30:45.123Z",
  "user": "JSMITH",
  "tool": "query_customer_data",
  "parameters": {"customer_id": "12345"},
  "result": "success"
}

​
Getting Started with MCP

If you’re new to MCP, here’s your learning path:
1

Start with the Basics

Begin with our Quick Start Guide to get a basic MCP server running in your environment and test your first tool execution.
2

Understand the Architecture

Review the Server Architecture to understand how components work together and the “Logic Throws, Handler Catches” pattern.
3

Create Your First Tools

Follow the SQL Tools Guide to create custom tools for your specific needs using YAML configurations.
4

Build an Agent

Explore Agent Development to create AI agents that understand your business context and effectively use your tools.
5

Deploy to Production

Use our Production Deployment guide for enterprise deployment with security, monitoring, and the Configuration Reference for environment variables.

​
MCP vs. Traditional Integration Approaches

Understanding how MCP compares to traditional integration methods helps illustrate its value:
  • Traditional REST APIs
  • Database Direct Access
  • Data Exports/ETL
Challenges:
  • Each AI tool needs custom integration
  • Broad API access often required
  • Complex authentication for each endpoint
  • Limited built-in security controls
MCP Advantages:
  • Universal protocol works with any MCP client
  • Granular tool-level access control
  • Built-in authentication and authorization
  • Standardized security model
MCP’s Key Innovation: MCP enables AI agents to become intelligent participants in your existing business processes rather than external tools that need special integration. They can ask for specific data when needed, perform authorized operations, and maintain the same security standards as human users.

​
Industry Examples and Use Cases

MCP is being adopted across various industries for different purposes:

Manufacturing

Real-time Production Monitoring
  • AI agents monitor production metrics
  • Automated quality control analysis
  • Predictive maintenance scheduling
  • Supply chain optimization

Financial Services

Compliance and Risk Management
  • Automated regulatory reporting
  • Risk assessment and monitoring
  • Fraud detection and analysis
  • Customer service automation

Healthcare

Patient Data Analysis
  • Clinical decision support
  • Patient outcome tracking
  • Resource utilization optimization
  • Regulatory compliance reporting

Retail & Distribution

Inventory and Sales Intelligence
  • Demand forecasting
  • Inventory optimization
  • Customer behavior analysis
  • Pricing strategy optimization

​
Common Questions from IBM i Professionals

No. MCP enhances your security by providing controlled, audited access. AI agents authenticate as specific users and are subject to the same authority checking as any other user. All operations are logged and can be monitored.
No. The MCP server acts as a secure intermediary. Your database remains protected behind your firewall, and the MCP server only exposes specific, pre-defined operations that you configure and control.
MCP servers are designed for efficient operation. SQL tools include row limiting, connection pooling, and resource management. You maintain full control over what operations are available and how they’re executed.
MCP supports comprehensive audit logging, including user identity, requested operations, parameters, and results. This often provides better audit trails than traditional approaches while maintaining compliance requirements.
Yes. The IBM i MCP Server includes OpenTelemetry integration for metrics and tracing, structured logging for SIEM integration, and health check endpoints for monitoring systems.

​
Next Steps

Ready to explore MCP for your IBM i environment?

Quick Start

Get your first MCP server running in 15 minutes

SQL Tools

Learn how to create powerful IBM i data access tools

Agent Development

Build AI agents that understand your business

Production Setup

Deploy with enterprise security and monitoring
MCP represents a fundamental shift in how AI systems interact with enterprise data. Instead of requiring you to adapt your systems for AI, MCP enables AI to work with your existing infrastructure, security models, and business processes.