How MCP Changes Integrations

Integrations are often where AI systems collapse under their own weight. Each connector is built as a bespoke adapter, with prompts and schemas embedded directly into the logic. It works—until the third or fourth integration, when duplication, drift, and breakage start to pile up.

MCP (Model Context Protocols) change this equation. They formalize how context is represented and shared, which in turn shifts the role of integrations from “ad hoc plumbing” to “repeatable interface.”

Before MCP: The Connector Tangle

Without MCPs, integrations typically follow this pattern:

• Each data source gets a custom extractor.
• Prompt templates live inside the integration logic.
• Context is re-shaped differently for every agent.
• Debugging is localized: each connector breaks in its own way.

The result is a tangle of one-offs. You spend as much time harmonizing your integrations as you do building new ones.

After MCP: Protocols as Interfaces

With MCPs in place:

• Context Schemas
  Each integration maps raw data into a standardized schema (e.g. Entity, Event, Signal). Every agent consumes the same format.

• Separation of Concerns
  Extraction (get the data) is decoupled from interpretation (what the AI does with it). MCPs define the contract between them.

• Agent Portability
  Because MCPs standardize context, an agent built for one system (e.g., “Lien Monitor”) can be dropped into another without rewriting how it parses data.

• Centralized Governance
  Instead of debugging ten connectors, you debug the shared protocol layer. Errors become systemic, not idiosyncratic.

The Impact on Integrations

1. Fewer Connectors, More Adapters

   • Instead of writing ten different “tax lien scrapers,” you write one “tax lien MCP adapter” that maps into the protocol. Every agent consumes it the same way.

2. Predictable Behaviors

   • Agents no longer require custom prompts per integration. They see the same fields (recordedDate, amount, parties) no matter the county or vendor.

3. Composability

   • Integrations stop being standalone. They become building blocks in a wider orchestration, because everything speaks MCP.

4. Scalability

   • Adding a new county, CRM, or API doesn’t require rewriting business logic. It just requires mapping that source into the MCP schema.

Risks and Costs

• Up-Front Investment: Building MCP adapters takes longer than a quick-and-dirty integration.
• Schema Freeze: Once you define a protocol, changes are harder—everyone depends on it.
• Premature Abstraction: If you introduce MCPs too early, you may lock into the wrong schema before you understand your needs.

A Mental Model

Think of MCPs as doing for AI integrations what REST APIs did for web development.

• Before REST: every system spoke its own dialect.
• After REST: systems became interchangeable, because the contract was standardized.

MCPs provide that same standardization for model-to-context interactions.

Closing Thought

Integrations don’t disappear with MCPs—they get demoted. Instead of being the core of your architecture, they become simple translators into a shared language.

That shift—from connectors as custom pipelines to adapters into a protocol—is what makes scaling multi-agent systems possible.