When to use (and not use) Model Context Protocol

April 2, 2025

MCP is having a moment.

As LLM capabilities evolve beyond just generation, developers are exploring how to use chains of prompts and tools to plan, fetch, execute, and respond—especially across APIs and third-party systems. That's the core promise of MCPs: offload orchestration into the LLM, turning your prompt into the brain of your product.

(MCP refers to the Model Context Protocol, a proposed standard for how language models reason, plan, and act using structured tool definitions, memory, and external data APIs.)

MCP is being adopted as a foundational spec for agentic architectures by Anthropic, OpenAI and others—and will likely become a core part of how models interact with real-world systems.

And to be clear—there are cases where this makes sense.

If you're building an internal AI agent, a low-stakes automation, or a prototype that needs to call multiple APIs in sequence, MCPs can accelerate development dramatically. The flexibility and abstraction are powerful. You can sketch out entire workflows in natural language and ship something useful within hours.

But that doesn't mean MCPs should become the foundation of your application—or your AI agent.

At Unified.to, we work with dozens of companies building AI-native products, vertical SaaS tools, and customer-facing assistants. What we're seeing across the board: developers are overestimating what MCPs are good at, and underestimating the risks of offloading too much.

Here's why.

1. LLMs are powerful tools—not orchestration layers

LLMs are amazing at pattern recognition, text generation, and fuzzy reasoning. But they're not built for deterministic logic, predictable routing, or reliable orchestration. MCPs often push you to offload critical product behavior—like fetching data, triggering updates, or syncing systems—into prompt chains.

This introduces fragility and complexity:

• Business logic gets buried in prompt templates
• Observability and monitoring become opaque
• Debugging becomes trial and error

The LLM should power parts of your experience—not take over your entire backend.

2. Passing customer data to the LLM creates data governance risks

MCPs often require giving the LLM access to customer systems: either by embedding data directly in prompts or by using the LLM to call external APIs. That means handing off credentials, tokens, and sensitive business context to a third party.

In an enterprise or customer-facing environment, this breaks trust. Your customers expect your app to own the interaction—not to delegate it to a model vendor.

At Unified.to, we believe in using LLMs to generate value—but with a clear boundary: you orchestrate, you control access, you govern the flow. The LLM shouldn't get the keys to your customer's backend.

3. The more logic you offload, the thinner your product becomes

As Model Context Protocol-based architecture servers grow more capable, it's tempting to let the LLM plan, reason, and execute across tools. But the more core functionality you shift into the LLM, the less your application owns—and the less value you have.

• You're not designing workflows—you're prompting them.
• You're not orchestrating—you're hoping the model gets it right.
• You're not building an opinionated product—you're handing that over to someone else's general-purpose model.

This leads to thinner applications that rely too heavily on external orchestration, not external data. And that's an important distinction.

At Unified.to, we believe using third-party data is a superpower—as long as you own the logic that powers how it's used. That's what makes AI-native SaaS sticky: not just access to customer systems, but smart, reliable, product-controlled workflows built on top of that access.

4. AI works better with structured data

AI systems rely on clean, contextual, and structured data to work effectively. Whether you're classifying support tickets, summarizing conversations, or triggering automation based on HR events, messy or inconsistent inputs will add complexity and reduce reliability.

That's where Unified.to comes in.

We unify customer data from third-party applications into consistent schemas that your product—and your AI features—can trust. For example, in messaging platforms, we normalize the core concept of a "message body," so you don't have to build custom extractors for Slack, Discord, Teams, and more.

With support for real-time sync, historical lookups, and normalized fields, Unified.to gives your AI stack a reliable foundation—without the complexity of integrating every service individually.

• Unified access to CRMs, HR tools, accounting systems, file storage, messaging platforms, and more
• Real-time data when your assistant or workflow needs it
• Governed access and full observability
• No need to build or maintain dozens of brittle APIs

5. The real unlock is AI-native architecture—not MCP-native design

To be clear: we're not anti-MCP. There are cases where chaining LLM steps makes sense—especially for internal agents or constrained environments. But the current hype makes it seem like every team should be building this way.

You don't need to follow the MCP playbook to build AI-native software. The best products we see combine:

• Traditional logic and workflows
• LLM-powered enhancements
• Unified access to the customer's business systems

These apps are fast, flexible, and user-friendly—with smart defaults, deterministic behavior, and human-in-the-loop controls where it counts.

TL;DR

MCPs sound compelling—but don't let the hype steer you away from product ownership.

• Use LLMs where they shine (text generation, classification, fuzzy matching)
• Maintain control over logic, data access, and orchestration
• Leverage a unified API to connect with your customers' tools reliably

This is how you build AI-native SaaS that lasts—not just another thin wrapper on top of a prompt.

Want to build smarter, more opinionated AI features?

Start your free 30-day trial or talk to our team about unifying your data layer for LLM-powered applications.