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The MCP Integration Pattern

Model Context Protocol: the integration pattern that lets AI agents talk to enterprise systems. What it is, why it matters, and how to implement it.
18 May 2025·8 min read
Mak Khan
Mak Khan
Chief AI Officer
John Li
John Li
Chief Technology Officer
engineeringenterprise-ai
Every enterprise AI project hits the same wall: the model is smart, but it cannot do anything. It cannot read from your CRM, update your ERP, trigger a workflow, or check a policy. Model Context Protocol changes that. It is the integration standard that turns AI models from conversationalists into operators, and it is the pattern we are betting on for enterprise AI integration.

The Integration Problem

AI models are remarkably capable in isolation. Give them text, they reason about it. Ask them questions, they answer them. But enterprise value requires action, not just answers.
A customer service AI needs to look up order status, process returns, and update tickets. A compliance AI needs to check documents against regulatory databases. A procurement AI needs to query supplier systems and create purchase orders.
The traditional approach is custom integration for each system. Write API wrappers. Build tool-calling functions. Handle authentication, error cases, and data formatting for every connection. It works, but it does not scale. By the tenth integration, you are maintaining a fragile web of custom code.
68%
of enterprise AI pilots fail to move to production, with system integration cited as the primary blocker
Source: McKinsey, The State of AI, 2024

What MCP Is

Model Context Protocol is an open standard, introduced by Anthropic in late 2024, that defines how AI models interact with external systems. Think of it as a USB port for AI. Instead of building custom connectors for every system, you build MCP servers that expose capabilities in a standard format.
The architecture is straightforward:
  • MCP Servers wrap existing systems (databases, APIs, file systems, business applications) and expose their capabilities in a standard format.
  • MCP Clients are the AI applications that consume those capabilities.
  • The Protocol defines how clients discover available tools, request actions, receive results, and handle errors.
The key insight is separation of concerns. The team that builds the CRM integration does not need to know about the AI model. The team that builds the AI application does not need to know the CRM's API details. The protocol mediates.

Why It Matters for Enterprise

Standardised Integration

Before MCP, every AI integration was bespoke. Team A builds a Salesforce connector for their customer service AI. Team B builds a different Salesforce connector for their sales AI. Both work. Neither is reusable. When Salesforce updates their API, both break independently.
With MCP, you build one Salesforce MCP server. Every AI application in the organisation connects through it. One integration point. One place to update. One place to manage access control.

Composable AI Capabilities

MCP servers are composable. An AI agent can discover and use multiple MCP servers simultaneously. A procurement AI might use an ERP server, a supplier database server, and a compliance server in a single workflow. Adding a new capability (say, budget approval) means adding an MCP server, not rewriting the AI application.
This composability is transformative for enterprise AI. It means AI capabilities compound. Each new MCP server you build makes every AI application in your organisation more capable.

Security and Governance

MCP includes built-in patterns for authentication, authorisation, and audit. Every tool call goes through the protocol, which means you have a single point for access control, logging, and monitoring.
In enterprise contexts, this matters enormously. You can answer questions like: which AI applications accessed the CRM today? What data did they read? What actions did they take? Without MCP, answering these questions requires aggregating logs across multiple custom integrations.

Implementation Patterns

Pattern 1: The Wrapper

The simplest pattern. Wrap an existing API in an MCP server.
You already have a REST API for your inventory system. Build an MCP server that exposes the relevant endpoints as MCP tools. The AI model calls the MCP tools. The MCP server translates to API calls. The inventory system does not change.
This is where most enterprises should start. Do not rebuild your systems. Wrap them.

Pattern 2: The Aggregator

Multiple data sources behind a single MCP server. A "customer context" MCP server that queries the CRM, the support ticketing system, and the billing system to provide a unified view.
The AI model does not need to know about three systems. It asks the customer context server for information about a customer and gets a complete picture. The aggregation logic lives in the MCP server, not in the AI application.

Pattern 3: The Workflow

MCP servers that orchestrate multi-step business processes. A "procurement workflow" server that handles the entire purchase order process: check budget, validate supplier, create order, route for approval.
The AI model calls a single tool ("create purchase order") and the MCP server handles the complexity. This pattern is powerful but requires careful design around error handling and rollback.

Pattern 4: The Guardian

MCP servers that enforce business rules and compliance. A "compliance guardian" server that validates actions against policy before they execute. The AI model proposes an action, the guardian checks it, and only approved actions proceed.
This pattern is essential for regulated industries. It separates AI capability from business governance.

What We Have Learned

We have been building MCP integrations since early 2025, and several patterns have emerged:
Start with read-only. Build MCP servers that read from enterprise systems before building ones that write. Read operations are lower risk and give you confidence in the pattern before you enable actions with consequences.
Design for discovery. MCP includes a tool discovery protocol. Make your tool descriptions clear and specific. The AI model uses these descriptions to decide which tools to call. Vague descriptions lead to wrong tool selection.
Handle errors explicitly. Enterprise systems fail in enterprise ways: timeouts, authentication expiry, rate limiting, maintenance windows. Your MCP server needs to handle these gracefully and return useful error messages that the AI model can act on.
Monitor everything. Every MCP tool call should be logged with the requesting application, the user context, the inputs, and the outputs. This is your audit trail and your debugging toolkit.
The first time an AI agent successfully queried our client's ERP, created a purchase order, and routed it for approval through MCP, the client's CTO just stared at the screen. Because it worked with their existing systems, not alongside them.
Mak Khan
Chief AI Officer

The Practical Path

If you are starting with MCP:
  1. Identify your highest-value integrations. Which enterprise systems would make your AI most useful if it could access them?
  2. Build a read-only MCP server for your most-used system. Get comfortable with the pattern.
  3. Add write capabilities with appropriate guardrails. Human-in-the-loop approval for high-impact actions.
  4. Compose servers into multi-system workflows. This is where the real value emerges.
  5. Establish governance around MCP server deployment, access control, and monitoring.
MCP is not the only integration approach, and it is still maturing. But the principle it embodies, standardised, composable, governable AI-to-system integration, is the direction enterprise AI is heading. Building in this direction now positions you well regardless of which specific standard wins.