Updated on 23rd April 2026 by Fraser Davidson

Model Context Protocol, or MCP, is still in its early hype cycle, but the questions around it are getting more serious.

Right now, most SaaS teams are focused on the first wave of MCP decisions:

• What should we do with MCP?
• How much will it cost in tokens?
• Is MCP better than CLI-based integrations?
• What will customers actually want to do with it?

Those are valid day-one questions. But they are still day-one questions.

If AI is here to stay, and most of us now accept that it is, then MCP is likely here to stay too. The more important question is no longer whether MCP matters. It is whether the way you implement it today will still work when adoption grows tomorrow.

MCP is not just a technical feature

It is tempting to evaluate MCP purely through a technical lens.

Teams compare MCP with CLI approaches. They look at token efficiency and assess implementation patterns. They think about the fastest way to enable an initial use case.

That makes sense at the start. But it can also lead to a narrow view of what MCP is really doing inside a SaaS product.

MCP is not just an interface for developers. It is a scalable way to deliver AI interoperability to a much broader customer base.

CLI-based approaches may absolutely have a place. In some scenarios, they may be more token-efficient. They may also be ideal for highly technical users such as developers, implementation specialists, or internal teams comfortable working close to code and command lines.

But most SaaS businesses do not serve only deeply technical customers.

If you want to bring AI-powered interoperability to the mass market of users, you need a delivery model that is easier to install, easier to understand, and easier to operationalize. That is where MCP becomes compelling.

MCP gives SaaS companies a scalable route to AI interoperability

For SaaS platforms, MCP creates a practical way to let customers use AI and LLMs to interact with product functionality in a more accessible way.

That matters because customer expectations are changing fast. Users increasingly want software to be interoperable with AI tools, agentic workflows, and model-driven experiences. They do not just want APIs in the background. They want usable AI-powered outcomes on top.

MCP helps bridge that gap.

It gives SaaS vendors a way to expose functionality to AI systems in a structured, installable, and increasingly standardized format. That makes it easier to move beyond one-off experiments and toward repeatable customer-facing capability.

And while concerns about token usage are real, they should not be treated as fixed constraints. MCP efficiency will improve over time. In fact, teams can already make meaningful gains by:

• focusing MCP servers more tightly on specific use cases
• improving readability and metadata
• optimizing prompts and skills
• combining MCP with traditional API-to-API integrations for regular or high-volume syncs and data movement

The point is not that MCP replaces every other integration pattern. It does not.

The point is that MCP expands the way SaaS platforms can deliver AI interoperability, especially for customers who need something more accessible than a developer-first tooling model.

The same pattern has played out before

This is not the first time the industry has gone through this cycle.

When APIs first became central to SaaS strategy, the early focus was on enablement. Teams wanted to know what they could build, how quickly they could launch, and what the first use cases would be.

That worked, for a while.

Then scale arrived. And with scale came a different set of questions:

• How do we govern usage?
• How do we support customers effectively?
• How do we monitor what is happening?
• How do we adapt the experience for different use cases?
• How do we move fast without losing control?

MCP is likely to follow the same path.

Today, companies are still in the experimentation phase. They are figuring out what MCP can do and where it fits. But once customer adoption grows, the center of gravity will shift from possibility to operational scale.

Tomorrow’s MCP conversation will be about scale

The next phase of MCP will be defined less by novelty and more by operational maturity.

That future conversation will revolve around five themes.

1. Control

As customers begin using MCP in real workflows, SaaS vendors will need guardrails.

Who can access what? Which functions should be exposed? What limits should exist around actions, data, or environments? How do you ensure MCP usage aligns with customer entitlements, security expectations, and platform governance?

Early MCP implementations can afford to be permissive. Scaled ones cannot.

2. Enablement

Giving customers access to MCP is not the same as helping them succeed with it.

Enablement will matter more than raw capability. Vendors will need onboarding patterns, templates, documentation, recommended use cases, and support structures that help customers get value quickly without requiring deep technical expertise.

The winners will not just expose MCP. They will make it usable.

3. Observability

Once MCP becomes part of real customer workflows, visibility becomes essential.

You will want to know:

• how MCP is being used
• which use cases are driving value
• where failures or inefficiencies occur
• what is creating unnecessary cost
• which patterns should be optimized or scaled further

Without observability, MCP becomes hard to govern and harder to improve.

4. Customisation

No two customers will want to use AI in exactly the same way.

Some will need narrow, highly controlled workflows. Others will want flexibility. Some will prioritize speed, others compliance, others cost efficiency.

That means SaaS vendors will need ways to tailor MCP experiences without rebuilding everything from scratch. Customisation will become a competitive differentiator.

5. Agility

This market is evolving too quickly for rigid architectures.

Models are changing. Standards are changing. Customer expectations are changing. Integration patterns are changing.

The SaaS teams that succeed with MCP will be the ones that can adapt quickly, refining server design, prompt strategies, orchestration logic, and delivery models as the landscape matures.

Day one decisions shape day two outcomes

This is the part that is easiest to miss.

It is natural to focus on immediate implementation challenges. Every team does it. But if your day-one MCP strategy is designed only to get something live, you may create a day-two scaling problem that is much harder to solve.

A short-term solution can become a long-term constraint.

That is why SaaS leaders should think beyond the first use case. Not because experimentation is wrong, but because architecture, governance, and delivery choices made early tend to echo later.

If MCP is going to become a meaningful layer in how customers interact with SaaS through AI, then it needs to be built with scale in mind from the start.

The real question is not whether to use MCP

For many SaaS companies, the real question is no longer whether MCP is interesting.

It is whether they are preparing for the stage that comes after the first proof of concept.

MCP is a powerful route to enabling customers to use AI and LLMs with your platform. But enablement without control is risky. Accessibility without observability is fragile. Early momentum without a scaling plan does not last.

So yes, solve the day-one challenge. But do not stop there.

Because today you may be wondering what to do with MCP. Tomorrow, you will be wondering how to scale it.

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