Systems | Development | Analytics | API | Testing

Introducing the Kong MCP Registry: Connect AI Agents with the Right Tools

In the rapidly evolving landscape of AI-driven development, the Model Context Protocol (MCP) has emerged as the critical standard for connecting AI applications to the data and tools they need. We are excited to announce the Technical Preview (TP) of Kong MCP Registry, a major milestone in our mission to provide the most comprehensive platform for modern API and AI management.

Connect Your Local AI Model to Enterprise Databases with DreamFactory: A Real-World Integration Story

A mid-sized enterprise had a straightforward but powerful idea: use their locally-hosted AI model to automatically generate summaries of employee performance review data stored in their SQL Server database. The workflow seemed simple enough: The reality? This "simple" integration touches on some of the thorniest problems in enterprise software: database security, API orchestration, authentication, timeout management, and reliable data transformation.

Why transparent AI is the only AI you can trust in QA

AI fosters speed. Transparency fosters confidence. AI for QA testing is suddenly everywhere. Every tool claims it’s “AI-powered.” Every demo promises smarter test generation, faster maintenance, and fewer bugs. Plus, with AI accelerating the pace at which developers write and ship code, QA leaders are under growing pressure to keep up. It makes sense that teams are looking for AI for QA testing. But here’s the uncomfortable truth: AI in QA only works if you can trust it.

Scaling Personalization Engines Without Scaling Risk

Personalization engines sit at the core of most modern digital platforms. From content ranking to feature recommendations, AI-driven personalization shapes how users experience products at scale. When these systems work well, they feel invisible. Engagement improves, friction drops, and platforms grow efficiently. But as personalization engines scale, so does their influence, often in ways engineering teams do not fully anticipate at the outset.

The Agentic Analytics Leap: How AI Agents Are Upgrading Your BI Team

Your data team is drowning. They spend 80% of their time on repetitive reporting and only 20% on strategic analysis. You hired them to be analysts, but they’re stuck being report builders. Every Monday morning is the same: pull the numbers, update the spreadsheet, format the email, send it out. Rinse and repeat.

AI in QA: Moving Beyond Hype to Execution in 2026

The development of software is becoming shorter. What took months is now done in weeks or even days. Traditional tests in high-speed environment have been found to act as bottlenecks, which slows down the software release process cycles. Here is where Artificial Intelligence comes in, not only as a new product, but as a very essential infrastructure of the modern Quality Assurance.

Chat with Your Data: The Official Databox MCP

We are thrilled to launch the official Databox MCP (Model Context Protocol). This open standard server bridges the gap between your business data and your favorite AI tools, turning general-purpose LLMs into specialized data analysts that know your business data. Stop manually exporting CSVs or taking screenshots of dashboards. With Databox MCP, you can connect 130+ data sources (Google Analytics, HubSpot, Salesforce, Stripe, and more) directly to tools like Claude, ChatGPT, Cursor, and n8n.

How an AI Assistant Can Work With Your Business Data with MCPs

And instead of getting a generic answer or being told to check your dashboard, the AI pulls the exact numbers from your company’s data and gives you a real answer in seconds. This is no longer science fiction. A new technology called MCP (Model Context Protocol) makes this possible. It’s a standardized way for AI tools to securely connect to your business intelligence and analytics platforms and actually work with your real data.