MCP Servers: The Technical Debt That Is Coming

Over the last decade, we’ve refined how APIs are built, shared, and consumed. REST became a common ground, OpenAPI offered structure, and gRPC brought speed. But now, in the age of AI, something new is surfacing: the rise of MCP servers — Model Context Protocol servers.

These systems offer an enticing promise: bring AI into the loop by orchestrating backend calls, shaping flows in natural language, and empowering LLMs to act more independently.

But with great power comes... a serious risk of technical debt.

Let’s unpack what’s driving the popularity of MCP servers, why developers are leaning on them, and where the cracks are already showing.

Why MCP Servers Are Gaining Momentum

At a glance, MCP servers seem like the logical next step in the AI evolution of backend systems. They typically:

• Accept natural language or prompt-based instructions
• Orchestrate backend APIs, databases, and services
• Return responses designed for LLMs or human consumption
• Abstract away infrastructure complexity for rapid iteration

This appeal is strongest among teams grappling with:

• Undocumented or poorly documented APIs
• Rigid, slow-moving backends
• Complex business logic scattered across services

The result? A shift away from service boundaries toward a new monolithic orchestration layer designed to speak the language of LLMs.

Yes, it’s fast. It works. But only at first.

What’s Missing from the Current API Ecosystem?

1. Lack of Business Flow Documentation

APIs tell you how to call something, not why.

For example:

• What does a CreateOrder API really do behind the scenes?
• When should a shipment trigger?
• What systems are impacted?

This lack of intent and flow forces devs — and now LLMs — to guess. The result? Fragile integrations, endless Slack threads, and broken assumptions.

2. APIs Aren’t AI-Friendly (Yet)

OpenAPI, gRPC, and GraphQL are all human-readable, but not human-understandable.

Without the following, LLMs would struggle to use APIs optimally or safely:

• Natural language annotations
• Usage examples
• Preconditions/postconditions

That’s where MCPs come in: they embed logic and intent directly in prompt-driven flows.

But in doing so, they might also sidestep decades of architecture best practices.

The Hidden Risks of the MCP Server Pattern

Used responsibly, MCPs can speed up AI agent development. But they also come with serious drawbacks that echo past mistakes.

1. Bypassing Layered Architectures

We're already seeing MCPs:

• Querying databases directly
• Making filesystem or shell calls
• Calling internal services in uncontrolled ways

This breaks the separation of concerns. Everything gets funneled through a single orchestrator — the MCP.

2. Reinventing the Monolith (Now With Prompts)

When orchestration, logic, and data access live in an MCP:

• Flows become hard to debug
• Business logic hides inside prompt templates
• There's no versioning or ownership
• You get a new kind of monolith

And the worst part? You won’t notice it until it’s too complex to refactor.

3. Long-Term Maintenance Nightmares

The short-term gains are undeniable. But over time:

• Every prompt edit is a risk
• Dependencies become hardcoded and tangled
• The MCP becomes your biggest bottleneck

You're trading developer velocity today for operational complexity tomorrow.

Remember BPEL? This Isn’t Our First Time

If you’ve been around long enough, this might feel familiar.

BPEL and ESBs (Enterprise Service Buses) once promised:

• Drag-and-drop workflows
• Centralized orchestration
• Integration without coding

But in reality, they:

• Created brittle deployments
• Became single points of failure
• Slowed down innovation

Eventually, we moved to:

• Microservices
• Domain-driven design
• Event-driven architectures

MCPs are at risk of repeating these mistakes under the modern AI banner.

What Should We Be Doing Instead?

MCPs are trying to solve real problems, but there are better paths forward.

1. Improve API Documentation

Invest in:

• Human-readable descriptions of why and when to use an API
• Examples that show workflows and decision-making
• Natural language annotations for LLM consumption

Let LLMs reason with context, not just syntax.

2. Embrace Event-Driven Architectures

Instead of central orchestration:

• Use events to trigger reactions
• Let services act autonomously
• Avoid chaining APIs in single flows

This promotes resilience and testability.

3. Use Existing Integration Patterns

Patterns from enterprise integration still apply:

• Content-based routing
• Message transformation
• Retry policies
• Circuit breakers

Wrap them in LLM-friendly wrappers — but don’t ignore them.

4. Govern MCP Usage Carefully

If you must use MCPs:

• Don’t treat them as systems of record
• Avoid direct DB or filesystem access
• Extract reusable logic into APIs
• Version your prompt flows like real software

Treat them like an interface, not an architecture.

Can We Shift the MCP Hype in the Right Direction?

Yes — but only with discipline.

MCPs solve pain points: bad contracts, brittle flows, opaque systems. Used wisely, they help us experiment with intelligent agents and bridge AI with legacy systems.

Used recklessly, they become the ESB of the AI era — and we’ll spend a decade untangling them again.

Final Thoughts: Build for the Long Game

The promise of MCPs is real.

But architecture isn’t about today — it’s about five years from now, when:

• Your team has changed
• Your flows are mission-critical
• Your LLM agents are embedded in user-facing apps

Before you go all-in, ask yourself:

• Where should business logic really live?
• Are we building for scale and change, or just for the demo?
• Will this MCP make us faster in 12 months, or slower?

Prompts can move fast. Tech debt moves faster.

What do you think? Are MCPs a breakthrough or a ticking time bomb? Let’s talk.