Understanding MCP

A practical introduction to the Model Context Protocol (MCP) for IBM i professionals—what it is, why it matters, and how it works.

MCP is an open standard that connects AI agents to data sources through a universal protocol. Instead of building custom integrations for each AI tool, you implement MCP once and it works with Claude, VSCode, Cursor, and any other MCP-compatible client.

Think of it like HTTP for AI agents: HTTP is the universal protocol for web browsers. MCP is becoming the universal protocol for AI agents to access external data and tools.

Official MCP Documentation

Comprehensive protocol specification and guides

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MCP in Three Sentences

1. MCP is a standardized protocol that lets AI agents discover and use tools you provide
2. One server, all clients - Build once, works with any MCP-compatible AI tool
3. Security first - Controlled access, authentication, and audit trails built into the protocol

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Why MCP Matters for IBM i

MCP lets AI agents access IBM i data while respecting your existing security model:

• ✅ SQL runs as the authenticated user - IBM i object authorities apply
• ✅ Audit trails maintained - All operations logged with user context
• ✅ Data stays on IBM i - No ETL pipelines or data copies required
• ✅ Standard tooling - OpenTelemetry, structured logging, compliance-ready

Real-world use cases:

• Ask AI “What jobs are consuming CPU?” → Queries QSYS2.ACTIVE_JOB_INFO
• Ask AI “Show me sales trends” → Runs your existing SQL queries
• Ask AI “Check system health” → Executes monitoring queries you define

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How MCP Works

The Flow:

1. AI agent requests a tool - “I need to query active jobs”
2. MCP server validates and executes - Checks auth, runs SQL as authenticated user
3. IBM i returns data - SQL results from DB2 for i or QSYS2 services
4. Agent receives formatted response - Structured JSON the AI can understand

For detailed protocol specifications, see the official MCP documentation.

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MCP Core Concepts

MCP defines three main primitives. For IBM i, tools are most important:

Tools

Operations AI agents can execute

For IBM i: SQL queries, stored procedure calls, system commands

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Resources

Static content AI agents can read

For IBM i: Documentation, schemas, configuration files

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Prompts

Reusable prompt templates

For IBM i: Common query patterns, analysis templates

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Example Tool (YAML):

tools:
  system_status:
    description: "Get IBM i system performance metrics"
    statement: "SELECT * FROM qsys2.system_status_info"

This becomes a tool AI agents can discover and call automatically.

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IBM i MCP Server: What Makes It Different

Our implementation adds IBM i-specific features to the standard MCP protocol:

• YAML SQL Tools - Define database operations in simple YAML (no coding required)
• IBM i Authentication - Per-user authentication with RSA encryption
• Authority Respect - SQL runs as the authenticated user, IBM i authorities apply
• Production Features - OpenTelemetry, structured logging, audit trails

For detailed architecture and implementation details, see Server Architecture.

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MCP vs. Traditional Integration

Approach	MCP	REST APIs	Direct DB Access
Client Support	Universal	Custom per client	Custom per client
Security	User-level auth	Varies	Service account
Data Movement	Query in place	Often requires ETL	Direct access
Audit Trail	Built-in	Custom	Database logs only
Setup Complexity	Once	Per client	Per client

Key Difference: MCP is a standard protocol. Build once, works with any MCP-compatible AI client—today and in the future.

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Next Steps

Ready to try MCP with IBM i? Here’s your path:

Quick Start (15 min)

Install the server and run your first SQL tool

Server Architecture

Understand how SQL becomes AI tools

Build SQL Tools

Create custom tools using YAML

Connect AI Clients

Integrate with Claude, VSCode, Cursor, etc.

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Learn More About MCP

Official MCP Docs

Protocol specification and reference

MCP GitHub

SDKs, examples, and community resources

The MCP Advantage: One standard protocol, universal AI client compatibility, enterprise security built-in. Your IBM i data becomes accessible to AI agents while maintaining your security and compliance standards.