Systems | Development | Analytics | API | Testing

If you’re evaluating MCP servers for your analytics stack, you’ve probably noticed that “MCP support” can mean very different things depending on the vendor. I’ve been working with both platforms, and the distinction matters more than most comparison articles let on. Supermetrics and Databox both offer MCP implementations, but they’re built for different jobs.

As AI continues to rise in importance across all industries, the cost of implementation, readily available access to cloud computing, and practical business use cases make AI-powered offerings a competitive advantage for product managers, engineering, and data leaders. However, AI isn’t without its fair share of risks and challenges.

TL;DR: DreamFactory 7.4+ includes a built-in MCP (Model Context Protocol) server that lets you connect any LLM—ChatGPT, Claude, Perplexity, or custom AI agents—to your enterprise databases through governed, role-based APIs. Setup takes minutes: create an MCP service in the admin console, copy the OAuth credentials, and point your AI application to the generated endpoint.

For today’s CTOs and CIOs, the pressure to deliver actionable data insights within your products has never been higher. However, a critical dilemma often stalls your progress toward the business intelligence tools you need for the task: Should your engineering team build a bespoke analytics engine from scratch, or should you integrate a professional embedded solution?

Agentic AI is moving quickly from experiments to real work. In 2026, it shows up inside the workflows that drive outcomes: decisions, operations, and accountability. In the season 7 premiere of the Data Chief podcast, host Cindi Howson sat down with three leaders who work at the intersection of AI ambition and enterprise execution: Paul Baier (GAI Insights), Jennifer Belissent (Snowflake), and Rory Blundell (Gravitee).

With AppSignal's MCP Server and Zed AI, it's easier than ever to ship code with confidence, while cutting out time-consuming context switching. In this article, we'll show you how to master your coding workflow with the help of AppSignal's MCP Server and Zed's AI editor.

The impact of AI on software quality is no longer theoretical, it’s already here. For engineering leaders, this shift represents more than a technical upgrade, it’s a cultural and strategic one. AI is transforming how teams approach quality, enabling faster decisions, improved visibility, and more intelligent prioritization across every stage of the development lifecycle. Traditionally, software quality was managed reactively. Teams waited for issues to surface and then fixed them.