What Are MCPs? The New Standard Powering Next-Gen AI Integrations

AI is actively evolving from reactive chatbots to proactive digital workers. Instead of just answering questions, AI agents can navigate between tools, access live data, and trigger operations across your entire tech stack. One key enabler you might have heard about recently? Model Context Protocol (MCP).

MCP is quickly becoming a core part of how language models move from simply responding to actively working within business systems. You can think of MCP as an instruction manual format written specifically for AI. Just as APIs help developers understand what a system can do, MCP helps AI agents understand what actions are possible within a business system and how to execute them.

As organizations and services adopt MCP, AI agents will increasingly be able to navigate between tools, access live data, trigger operations, and do it all with minimal setup since MCP offers a standard approach.

Interest in MCP is growing quickly. Customers are asking about it. Companies are deploying it. As intelligent automation becomes a focus, MCP will become a core part of how AI gets integrated and put to work.

What Exactly Is MCP?

At a basic level, MCP is a structured way to describe what functions a system or tool offers. It’s written so that an AI agent can understand the available options. If Salesforce provided an MCP specification, it might include actions like "add_lead," "update_opportunity," or "generate_report," each with clear parameters and expected outputs.

The AI agent reads this specification and understands its options. No training required. No custom logic needed. It simply follows the instructions and gets the job done.

This is fundamentally different from traditional app integrations. MCP doesn't create direct connections between systems. Instead, it provides a common language that AI agents can interpret, making it possible to automate tasks across platforms more quickly and with less custom development.

Consider this scenario: A sales rep tells an AI agent, "Update the Johnson deal to closed-won and schedule our implementation kickoff." If your company’s CRM, email, and calendaring platforms have MCP enabled, the AI agent would automatically update the opportunity status, create a calendar event, and send automated notifications without extra help or custom logic.

Driving the Next Phase of AI Integration

Describing what a piece of software can do isn’t new. Developers have done that for years. What’s changed is who those descriptions are written for.

Instead of explaining features to other engineers, more teams are now documenting their systems in ways that AI agents can read directly. That small shift could have a major impact.

For engineering teams, MCP eliminates the need to build full and custom integrations for every AI use case. Squid AI now offers a native MCP connector, making it easier than ever to build AI agents that work across enterprise systems.

For business teams, MCP supports critical goals around automation, efficiency, and standardization. As MCP adoption becomes wider, it should enable non-technical staff to create workflows with minimal IT support, while ensuring consistent AI behavior across departments.

A common misconception is that MCP is just another app-connection method. It’s not. It doesn’t create any sort of direct connection. What it does is describe what’s possible, and it does it clearly.

If Airbnb had an MCP server, it might outline options like creating a listing, canceling a booking, or messaging a guest. The list of actions would come from Airbnb. The MCP itself just helps AI agents make sense of what’s on offer.

The Expanding AI Agent Landscape: Beyond MCPs

We’re entering a phase where AI systems are starting to do more than just respond to prompts or generate content. They’re beginning to interact with real-world software, and in some cases, with each other.

Another emerging standard generating interest is Agent-to-Agent communication, or A2A. The idea is simple: let two AI agents coordinate directly by passing structured messages. It’s a different use case than MCPs, which focus more on helping agents understand and use tools. A2A is more about collaboration between agents themselves. Imagine a customer service agent working with a scheduling agent to resolve a complex booking issue and coordinating behind the scenes using A2A, with each agent calling its own set of tools via MCP.

At Squid AI, support for both MCP and A2A is baked into our core platform and it’s powering deployments. That means our customers can build agents that understand system capabilities, make decisions, and even work together across tasks, all using standards that are new but quickly gaining adoption.

For enterprise use cases and regulated environments, security and compliance are still critical, and the MCP community is actively addressing key challenges around authentication, security, and compliance. While these areas continue to evolve, the direction is clear.

As AI systems mature, agents need reliable ways to understand their capabilities, coordinate with other systems, and operate safely within complex business environments. MCP provides that foundation today, enabling organizations to move beyond simple AI pilots toward true intelligent automation.

Ready to explore MCP for your organization? Start by identifying repetitive cross-system tasks that could benefit from AI automation. These often represent the highest-impact initial use cases for MCP implementation.

The future of AI integration isn't about building more connections—it's about creating better understanding. MCP is the bridge that makes that possible.