AI agents need a secure, standardized way to access your APIs. The Model Context Protocol (MCP) provides that bridge, and STOA Platform makes it trivial to deploy. In this tutorial, you'll learn how to set up a production-ready MCP gateway using Docker Compose in under 10 minutes.

New to MCP gateways? Start with our comprehensive guide: What is an MCP Gateway? to understand the architecture and security model before deploying.

By the end of this guide, you'll have a running gateway that exposes your existing REST APIs to AI agents like Claude, connects to authentication, and enforces runtime policies.

The Model Context Protocol (MCP) is a JSON-RPC 2.0 based protocol that standardizes how AI agents discover, authenticate with, and invoke external tools. It defines four phases — initialization, discovery, invocation, and streaming — over pluggable transports including SSE, WebSocket, and stdio. This article covers the protocol internals that matter for production deployments.

Three approaches dominate how AI agents call external tools in 2026: the Model Context Protocol (MCP), OpenAI Function Calling, and LangChain Tools. MCP is an open protocol for runtime tool discovery across any AI provider. OpenAI Function Calling is a proprietary API feature tightly integrated with OpenAI models. LangChain Tools is a framework abstraction that wraps tool definitions for orchestration pipelines. They solve different problems, operate at different layers, and can coexist in the same architecture.

WSO2 API Manager is an established open-source API management platform used by enterprises worldwide. As the API management landscape evolves — with AI agent protocols, Kubernetes-native architectures, and stricter European regulations — some teams are evaluating alternatives that better align with their current requirements. This guide provides a feature comparison, migration approach, and practical guidance.

Migrating from a legacy API gateway is one of the highest-stakes infrastructure projects an enterprise platform team can undertake. Done well, it eliminates years of accumulated technical debt, reduces licensing costs, and opens the door to AI agent integration. Done poorly, it disrupts production APIs and erodes trust with every team that depends on the platform.

This guide provides a vendor-neutral framework for planning and executing an API gateway migration in 2026 — covering assessment, policy translation, phased traffic migration, and the new requirements introduced by AI agents. Specific guidance for individual platforms (Broadcom Layer7, Software AG webMethods, Axway, Apigee) is linked throughout.

Migrating from IBM DataPower or TIBCO requires separating gateway routing from protocol-specific functions. This guide covers a sidecar approach: deploy STOA for REST/JSON traffic, federate identity via OIDC, and keep legacy systems for B2B protocols where they excel.

IBM DataPower and TIBCO BusinessWorks represent two of the most deeply embedded integration platforms in enterprise IT. Both handle critical workloads — security token services, multi-protocol mediation, B2B gateway functions — that organizations depend on daily.

This guide provides a practical assessment of migration approaches for organizations evaluating modernization paths from these platforms.

MuleSoft migrations work best when you separate the API gateway layer from integration flows. Move Experience APIs (routing, auth, rate limiting) to open-source gateways while keeping Anypoint for DataWeave transformations and complex connectors.

MuleSoft Anypoint has become one of the most widely deployed integration platforms in enterprise IT. Since Salesforce's acquisition in 2018, the platform has deepened its ties to the Salesforce ecosystem while organizations face evolving requirements around AI agent support, European data sovereignty, and infrastructure cost management.

This guide provides a practical assessment of when MuleSoft migration makes sense, what the challenges are, and how to approach it without disrupting existing integrations.

Connecting AI agents to enterprise APIs is the next frontier of digital transformation — and the next frontier of security risk. As organizations deploy AI agents built on Claude, GPT, Gemini, and open-source models, these agents need access to internal systems: databases, CRMs, ERPs, payment processors, and more. The question is not whether to grant this access, but how to do it without opening a new attack surface.

This article is part of the What is an MCP Gateway series. For the strategic context on why MCP matters for enterprise architecture, see ESB is Dead, Long Live MCP.

If you are evaluating an Apigee alternative, you are not alone. Since Google absorbed Apigee into its cloud platform, a growing number of organizations have found themselves facing rising costs, deepening vendor lock-in, and an increasingly opaque product roadmap. The good news: open-source API gateways have matured to the point where migration is not just feasible — it is often a strategic improvement.

Building a multi-tenant API gateway is one of the hardest infrastructure challenges in platform engineering. You need strong isolation between tenants, shared infrastructure for efficiency, and the ability to scale without multiplying operational complexity. After years of building multi-tenant API platforms — and applying those lessons to STOA on Kubernetes — here is what we learned.

This article is part of the Open-Source API Gateway 2026 series, covering architectural patterns for modern API infrastructure.