Where AI Meets Trust: The Actian MCP Advantage

With 25 years in the data industry, I’ve observed numerous “next big things” in the enterprise data stack. Some faded into mere buzzwords, while others quietly became essential. Currently, I think we’re approaching another pivotal shift—one that will distinguish organizations capable of effectively leveraging AI from those at risk of stagnation. The driving factor? The Model Context Protocol (MCP) and, crucially, how vendors are (or aren’t) adopting it.

The Context Gap in Enterprise AI

Ask any Chief Data Officer or data leader where AI initiatives struggle, and you’ll hear a familiar refrain: trust. In fact, a recent CDO Magazine report argued that turning data trust into business value was paramount with respect to building AI products. Large language models (LLMs) and AI assistants are powerful, but when they operate without enterprise context, they can hallucinate, misinterpret, or suggest actions that don’t align with how the business defines its world. By carrying definitions, lineage, and relationships, metadata grounds AI in a business context and prevents misleading or generic answers. A “customer” in finance may mean something entirely different in operations. Without context, AI is a blunt instrument.

This is where MCP comes into play. At its core, MCP is an open standard that enables AI agents to retrieve authoritative context from enterprise systems. Done right, it means AI doesn’t just crunch numbers or summarize text—it provides the much-needed context for LLMs or AI Agents to generate accurate responses.

But like many promising standards, execution varies. Some vendors have leaned in. Many haven’t. And that’s where differentiation matters.

What Actian Brings to the Table

With the launch of Actian MCP Server, context becomes a first-class citizen in AI workflows. This is not a proof-of-concept or an experiment on the fringe—it’s a core capability built into our Data Intelligence Platform.

Here’s what that means in practice:

• Live integration with metadata and lineage: AI agents don’t just connect to tables or APIs—they understand the definitions, lineage, and business context those assets carry.
• Dynamic data asset discovery: New data assets or updates surface instantly in connected AI environments like Copilot or Claude.
• Future-proof, standards-based design: Built on the open MCP standard, so you’re not locked into proprietary APIs or custom connectors.

And here’s the essence: metadata gives AI agents and LLMs the business context—definitions, lineage, and governance—that transforms them from eloquent guessers into trusted advisors.

How Other Vendors are (and aren’t) Responding

This space is still developing, but the competitive landscape is already taking shape.

• Open-source MCP servers are typically lightweight, and developer-friendly. However, they often serve a single purpose, require self-management, and lack enterprise features such as audit trails or lineage integration.
• Hyperscaler solutions like AWS Bedrock and Microsoft CoPilot allow their agents to connect to external MCP servers, but they do not provide one with built-in metadata governance. Instead, they depend on customers to handle this burden.
• Traditional catalog vendors have mostly stayed on the sidelines. Many still focus on embeddings, search, or AI wrappers but haven’t yet created a true MCP-compliant bridge to their metadata stores. In other words, “AI-ready” messaging without protocol-based execution.
• Hyped lists of “MCP tools” circulate online, but most options have a limited scope and are not enterprise-class infrastructure.

Currently, Actian is one of the few vendors combining MCP’s protocol advantages with a comprehensive, governed metadata infrastructure that enterprises require. A knowledge graph-powered data catalog, activated by a secure MCP server, is the non-negotiable foundation required to make AI safe, scalable, and valuable.

Why This Matters for Leaders

For CDOs, enterprise architects, and AI/ML leaders, the strategic stakes are high. Without an MCP server tethered to governed metadata:

• AI agents operate in silos, each building its own shadow version of “truth.”
• Definitions drift, trust erodes, and adoption slows.
• Every new AI tool demands custom connectors, increasing cost and vulnerability.
• Compliance risks escalate—AI outputs lack auditability and may fall short of emerging regulatory standards.

With Actian’s MCP Server:

• Agents ground their actions in contextual definitions and lineage.
• Governance remains intact while innovation accelerates.
• Future AI platforms plug into the same trusted backbone.

Bottom line: Context is no longer an afterthought. It’s the multiplier that turns AI into a trusted enterprise co-worker.

Over the past few years, I’ve seen many organizations overspend on AI pilots that failed because trust was never established. MCP helps avoid that trap. But it’s not enough to just follow protocol; what matters is how well it integrates into your enterprise data ecosystem.

That’s why Actian’s approach is different. We don’t just experiment; we embed MCP at the core of our data intelligence platform. For organizations serious about AI, this distinction is crucial.

Take a look at our new MCP Server functionality, and then let’s talk with you about how we can help you take your AI programs to the next level with confidence.

About Phil Ostroff

Phil Ostroff is Director of Competitive Intelligence at Actian, leveraging 30+ years of experience across automotive, healthcare, IT security, and more. Phil identifies market gaps to ensure Actian's data solutions meet real-world business demands, even in niche scenarios. He has led cross-industry initiatives that streamlined data strategies for diverse enterprises. Phil's Actian blog contributions offer insights into competitive trends, customer pain points, and product roadmaps. Check out his articles to stay informed on market dynamics.