What is Model Context Protocol (MCP) and IBM i MCP Server

Businesses increasingly turn to agentic AI intelligent systems that act autonomously, make decisions, and interact with tools — to drive modernization. IBM i platforms, known for reliability in core business applications, now embrace this wave through the Model Context Protocol (MCP). IBM’s dedicated MCP server for IBM i bridges cutting-edge AI agents with Db2 for i databases and system resources.

This comprehensive guide dives into the Model Context Protocol, explains how the MCP server operates on IBM i, and highlights practical applications that empower developers and administrators.

What Is the Model Context Protocol (MCP)?

Anthropic introduced the Model Context Protocol (MCP) in late 2024 as an open-source standard that standardizes communication between large language models (LLMs) and external tools, databases, and services. MCP functions like a “USB-C port for AI,” enabling plug-and-play integrations that allow AI agents to access real-time context without custom coding for each connection.

MCP tackles key challenges in agentic AI systems:

• Reliable tool calling and output parsing
• Error handling in multi-agent workflows
• Secure, structured data exchange

Developers use MCP to connect AI applications — such as Claude, Cursor, or custom agents — to resources like APIs, file systems, and databases. The protocol relies on JSON-RPC 2.0 over transports like stdio (local) or Server-Sent Events (SSE) for remote interactions.

How MCP Works: The Client-Server Architecture

MCP follows a clear client-server model that drives efficient AI interactions:

• MCP Host → AI applications (e.g., IDEs like VS Code with Copilot or Claude Desktop) process user requests and orchestrate tool calls.
• MCP Client → Embedded in the host, the client formats requests, manages sessions, handles errors, and parses responses. Popular clients include IBM BeeAI, Microsoft Copilot Studio, and Anthropic’s Claude.ai.
• MCP Server → Servers expose resources (read-only data), tools (actions with side effects), and prompts (reusable templates). They execute requests and return structured results to the LLM.

AI agents discover available tools, call them via natural language prompts, and receive streamed results. This setup empowers agents to reason, act, and adapt in dynamic environments.

The IBM i MCP Server: Bringing Agentic AI to IBM i

IBM released the official IBM i MCP server in beta in October 2025 (with ongoing updates through 2026) as an open-source project under Apache 2.0 license. Hosted on GitHub at IBM/ibmi-mcp-server, this server enables AI agents to interact securely with IBM i systems.

The server primarily provides SQL-based access to Db2 for i, leveraging Mapepire — IBM’s WebSocket database gateway — for low-latency connections. Administrators define tools in YAML files, exposing predefined queries for system monitoring, database operations, and more.

Key highlights:

• Built in TypeScript with production-grade features like observability (OpenTelemetry), caching, and modular design
• Supports HTTP transport for remote AI clients
• Integrates with frameworks like LangChain, Agno, and Google ADK

How the MCP Server Operates on IBM i

The IBM i MCP server bridges AI clients and IBM i:

1. AI Client Initiates Request — An agent (e.g., in Claude or a custom Python script) sends a JSON-RPC request to the MCP server endpoint.
2. Server Validates and Processes — The server loads YAML-defined tools, validates inputs, and routes SQL queries via Mapepire to Db2 for i.
3. Execution on IBM i — Mapepire authenticates and runs the SQL, retrieving data from system views like QSYS2.SYSTEM_STATUS or user tables.
4. Results Stream Back — The server formats and streams results to the client, where the AI agent interprets and acts on them.

This architecture ensures secure, controlled access — no arbitrary command execution, only admin-approved SQL tools.

Key Features of the IBM i MCP Server

The server delivers robust capabilities:

• Prebuilt Tool Collections — Ready-to-use YAML tools for performance monitoring, security auditing, job management, storage/IFS queries, and database metadata.
• Custom Tool Creation — Define new SQL-based tools easily in YAML.
• Deployment Options — Run via Node.js, Docker, Podman, or OpenShift.
• Performance Optimizations — Tool caching and streaming for fast agent interactions.
• Enterprise-Ready — HTTPS support, authentication, logging, and monitoring.

What You Can Do with the MCP Server on IBM i

The IBM i MCP server unlocks powerful agentic AI scenarios:

• System Monitoring and Troubleshooting — Ask an AI agent, “What’s the current CPU utilization?” It calls a performance tool and returns real-time metrics from QSYS2.SYSTEM_STATUS.
• Database Queries and Reporting — Agents execute complex SQL against business data — e.g., “List overdue orders from the last 30 days” — without manual coding.
• Job and Subsystem Management — Monitor active jobs, queues, and subsystems; automate alerts for issues.
• Security and Compliance — Query user profiles, authorities, and audit logs to detect anomalies.
• Storage and IFS Management — Analyze disk usage, IFS objects, and save files.
• Automated Administration — Build agents that apply PTFs, check system health, or generate reports using natural language.

Developers integrate these capabilities into tools like Project Bob (IBM’s AI code assistant for RPG/COBOL) or third-party agents for end-to-end automation.

Security Considerations for MCP on IBM i

Security remains paramount. The server uses:

• IBM i credentials for Mapepire connections (stored in environment variables)
• Tool restrictions to approved SQL only
• HTTPS for transport encryption
• Auditing via logs and OpenTelemetry

Admins control exposure, ensuring AI agents access only authorized data and operations.

The Future of AI Integration on IBM i

IBM plans to expand the MCP server with hundreds more tools in 2026, deepening integration with watsonx and other AI platforms. As MCP adoption grows across the industry, IBM i positions itself at the forefront of agentic AI, enabling businesses to modernize legacy systems intelligently.

Embrace MCP for Next-Gen IBM i AI

The Model Context Protocol and IBM i MCP server transform how AI interacts with IBM i environments. Developers now build intelligent agents that query Db2, monitor systems, and automate tasks securely and efficiently.

Explore the GitHub repository today, deploy the server, and start experimenting with agentic AI on your IBM i systems. The future of intelligent enterprise computing on Power Systems begins here.