How AI Actually Talks to Your Apps

ChatGPT has plugins and GPT Actions. Claude has MCP servers. Gemini has extensions. If you're confused about what any of this means for your business, you're not alone. Every few months there's a new acronym, a new announcement, and a new wave of LinkedIn posts declaring that everything has changed. It's a lot.

Here's the thing: the underlying idea is actually simple. The jargon just makes it sound more complicated than it is. So let's strip away the acronyms and talk about what's actually happening, what it means for people who run businesses and build automations, and where all of this is probably heading.

The Basic Idea

AI by itself can only read and write text. That's it. You send it words, it sends you words back. It can't send an email. It can't create a CRM record. It can't check your calendar or update a spreadsheet. By itself, it's a really good text processor sitting in a room with no doors.

To actually do things in the real world (send emails, create records, check calendars, update spreadsheets), it needs connectors. Bridges to external systems. Ways to reach out and touch your business tools.

That's what all of this jargon is about: different approaches to giving AI those bridges. The concept is simple even if the acronyms aren't. And if you've been building automations with tools like Zapier or Make, this should feel familiar. Those platforms have been building bridges between apps for years. The difference is that now the AI is the one deciding when and how to cross the bridge, instead of you defining every step in advance.

What Is MCP (Model Context Protocol)?

MCP is trying to do the same thing for AI connections. Instead of every AI platform building its own proprietary way to connect to tools, MCP defines a common protocol. An MCP server provides specific capabilities (read email, create tasks, search a database, pull CRM records) that any AI model can use when it needs to.

The key word is "open." MCP isn't locked to Claude. It's a published spec that other platforms are starting to adopt. OpenAI has signaled support. Other tools are building MCP compatibility. That matters because it means you (or your tool vendors) can build one MCP server and it works across multiple AI platforms, instead of building separate integrations for ChatGPT, Claude, Gemini, and whatever comes next.

In practical terms, an MCP server is a small service that says to the AI: "Here are the things I can do. Here's what inputs I need. Here's what I'll give you back." The AI reads that menu, and when it's working on a task that requires one of those capabilities, it calls the right tool with the right inputs. It's structured, it's predictable, and it's auditable (you can log every tool call).

We wrote a deeper piece on why every product company needs an MCP server if you want the vendor perspective. But for business users, the main takeaway is this: MCP is the emerging standard for how AI connects to things, and it's worth paying attention to.

How ChatGPT Connects to Tools

OpenAI's approach has gone through a few iterations. It started with plugins (remember those? they had a moment), then evolved into GPT Actions, and now there's a broader function-calling framework.

The way it works: you define an API spec (basically a document that describes what endpoints are available and what data they accept), ChatGPT learns what's available, and it calls those endpoints when relevant during a conversation. You ask ChatGPT to "find my upcoming meetings," and if there's a calendar action configured, it calls the right endpoint and returns the results.

It works. But it's tightly coupled to OpenAI's ecosystem. Setting up GPT Actions requires some technical chops (you need to write or provide an OpenAPI spec), and the original plugin marketplace never took off the way OpenAI hoped. The discoverability problem was real: even with hundreds of plugins, most users never found or used them.

For businesses already deep in the OpenAI stack (using the API, building custom GPTs, deploying through Azure OpenAI), GPT Actions is a viable path. But it's not an open standard. What you build for ChatGPT doesn't transfer to other AI platforms without rework.

How Claude Connects to Tools

Claude uses MCP servers and a capability called "tool use." The approach is more structured than OpenAI's plugin model and arguably more developer-friendly.

You define tools with clear schemas (what the tool does, what inputs it needs, what it returns). Claude evaluates the conversation, decides when a tool would be helpful, and calls it with the appropriate parameters. The protocol handles the back-and-forth, including cases where Claude needs to call multiple tools in sequence or use the output of one tool as the input to another.

What we find interesting about this approach is how explicit it is. Every tool has a defined interface. Every call is logged. The AI doesn't just "do things." It follows a structured protocol that makes it clear what's happening at each step. For businesses that care about auditability (and you should), that matters.

The ecosystem of MCP servers for common business tools is growing fast. CRMs, project management tools, databases, communication platforms, file storage. It's early, but it's moving quickly. And because MCP is an open standard, there's a natural incentive for tool vendors to build and maintain their own MCP servers, rather than waiting for each AI platform to build connectors for them.

How Gemini Connects to Tools

Google's approach is extensions, and it's philosophically different from OpenAI and Anthropic. Where those two are building open (or semi-open) ecosystems, Google is building depth within its own suite.

If your company runs on Gmail, Google Calendar, Drive, and Sheets, Gemini's native integrations are compelling. It can search your Drive, summarize your emails, check your calendar, and draft responses, all within the Google ecosystem. The experience is smooth because Google controls both the AI and the tools it connects to.

The tradeoff is obvious: it's great if you're a Google shop, limiting if you're not. If your CRM is HubSpot or Salesforce, your project management is in Asana, and your communication is in Slack, Gemini's native extensions don't help you much. Google has been adding third-party integrations, but the depth and breadth aren't comparable to what you get with MCP or even GPT Actions.

There's also a strategic question here. Google's approach is "we'll connect to everything in our own suite really well, and we'll gradually expand from there." Anthropic's approach is "here's an open standard, let the ecosystem build." OpenAI is somewhere in between. Which approach wins probably depends on whether you believe the future is one dominant ecosystem or an interoperable web of tools. (We'd bet on interoperability, but we've been wrong before.)

What This Means for Business Users

You don't need to understand protocol-level details. You don't need to know the difference between a function call and a tool use invocation. What you need to understand is this: AI can now directly interact with your business tools. That changes what's possible with automation.

Here's a concrete example. Today, if you want to automate "when a new deal is marked closed-won, create a project, assign tasks, and notify the team," you build a multi-step workflow in Zapier or Make. You define the trigger, map each action, set up the data transformations, and test it. It works great, and it runs reliably every time.

With AI connectors, you might eventually just tell the AI: "A deal just closed. Set up the project and let the team know." And the AI figures out which tools to call, what data to pass, and in what order. Less configuration up front. More flexibility for novel situations.

But (and this is a big but) it also raises questions that most organizations haven't thought through yet. What permissions does the AI have? What happens when it makes a mistake? How do you audit what it did? Who's responsible when it updates the wrong record? These aren't theoretical concerns. They're the same questions you'd ask if you hired a new employee and gave them access to every system in your company on day one.

We explore the practical tradeoffs of this approach in The Pros and Cons of Letting AI Connect Directly to Your Tools. The short version: the technology works, the governance is still catching up.

Where This Is Heading

These protocols are converging. MCP is gaining adoption beyond Anthropic. OpenAI has indicated they'll support it. Google is doing its own thing but will likely support interoperability at some point (they usually do, eventually). The direction is clear: AI that can interact with your entire tool stack through a common standard.

But we're early. Really early. We'd call this the dial-up era of AI connectivity. The modems are screeching, the connections drop, and downloading a picture takes forever. But you can see where it's going.

Here's what we think the next few years look like:

Near-term (now through late 2026): MCP becomes the de facto standard for AI-to-tool connections. Major SaaS vendors ship their own MCP servers. The AI can read data from most of your tools, but write actions still require human approval for anything important. Automation platforms like Zapier and Make add MCP support alongside their existing connectors.

Medium-term (2027-2028): AI connectors mature enough for routine write operations. "Create the project and assign the tasks" works reliably without human review for standard cases. Automation platforms evolve from "you build the workflow" to "you describe the outcome and the platform builds the workflow." But complex, multi-system orchestration still benefits from explicit workflow design.

Longer-term (2029+): The line between "automation platform" and "AI assistant" blurs significantly. Your AI assistant is your automation platform. But the underlying infrastructure (authentication, rate limiting, error handling, logging, monitoring) still looks a lot like today's iPaaS, just with a natural language interface on top.

We could be wrong about the timeline. We could be wrong about the specific path. But the direction seems clear: AI that can talk to your tools through open standards, with the governance and reliability infrastructure catching up over time.

For now, the pragmatic move is to keep building with the tools that work today (here's where to start), stay aware of how the connector landscape is evolving, and don't wait for the perfect future to start automating. The skills you build now (understanding data flows, thinking about edge cases, designing for reliability) will transfer to whatever comes next.

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This post is part of The SMB Automation Playbook, a series on practical automation for small and mid-size businesses.