The Enterprise Case for MCP

"We're spending millions on AI tools, but our employees still copy-paste data between applications." — Every CIO, 2024-2025

The Disconnect

Large organizations have invested heavily in AI:

• ChatGPT Enterprise licenses
• GitHub Copilot for developers
• Microsoft Copilot for Office
• Custom AI assistants and chatbots

Yet the productivity gains remain elusive. Why?

The AI can't access your data.

Your enterprise knowledge lives in:

• SQL databases and data warehouses
• CRM systems (Salesforce, HubSpot)
• Internal wikis and documentation
• Custom APIs and microservices
• File shares and document stores

None of these are directly accessible to your AI tools.

The Copy-Paste Tax

Watch any knowledge worker use ChatGPT for work:

1. Open ChatGPT
2. Ask about Q3 sales figures
3. ChatGPT says "I don't have access to your data"
4. Open Salesforce
5. Run a report
6. Copy the data
7. Paste into ChatGPT
8. Ask follow-up question
9. Realize you need more context
10. Open database tool
11. Run SQL query
12. Copy results
13. Paste into ChatGPT
14. Repeat 20 times per day

This pattern costs enterprises:

At a 10,000-person company, the copy-paste tax is millions of dollars per year.

The MCP Solution

The Model Context Protocol enables secure, direct connections between AI assistants and enterprise systems:

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│  AI Assistant   │     │                 │     │                 │
│   (ChatGPT,     │────▶│   MCP Server    │────▶│  Enterprise     │
│    Claude,      │     │   (Your Code)   │     │  Systems        │
│   Copilot)      │◀────│                 │◀────│  (DB, API, etc) │
│                 │     │                 │     │                 │
└─────────────────┘     └─────────────────┘     └─────────────────┘

Instead of copy-paste:

1. Open ChatGPT with MCP connections
2. Ask "What were our Q3 sales figures by region?"
3. ChatGPT calls your MCP server
4. MCP server queries Salesforce (with your permissions)
5. Returns structured data
6. ChatGPT analyzes and responds
7. Ask follow-up—MCP handles it automatically

What MCP Provides

We will dive deeper into the design of MCP server is lesson 4. Here is a quick overview:

Tools

Functions the AI can call:

• query_sales(region, quarter)
• create_ticket(customer, issue)
• generate_report(type, date_range)

Resources

Documentation the AI can read:

• salesforce://accounts/tiers
• jira://issues/severity-and-escalation
• s3://reports/quarterly/{year}

Prompts

Workflow templates for common tasks:

• "Customer health check" (combines multiple data sources)
• "Weekly standup summary" (aggregates JIRA, Git, Slack)
• "Compliance audit prep" (gathers required documentation)

Enterprise Requirements

Building a "hello world" MCP server is easy. Building one for enterprise is not.

Enterprise MCP servers must be:

Business Focused

• Easy to connect by non technical people (no local installation)
• Connected to the organization data fabric
• Domain specific (different per department)

Secure

• OAuth 2.0 authentication (no API keys)
• Integration with enterprise identity providers (Cognito, Okta, Entra)
• Audit logging for compliance
• Input validation to prevent injection

Reliable

• 99.9%+ uptime
• Graceful degradation
• Retry logic and circuit breakers
• Proper error handling

Observable

• Structured logging
• Metrics and dashboards
• Alerting on failures
• Performance tracking

Maintainable

• Type-safe implementation
• Comprehensive tests
• CI/CD pipelines
• Documentation

Scalable

• Handle concurrent users
• Cost-effective at scale and can scale to zero
• Global availability options

Why Most Tutorials Fail

Search for "MCP tutorial" and you'll find:

# A typical tutorial example
from mcp import Server

server = Server()

@server.tool()
def hello(name: str) -> str:
    return f"Hello, {name}!"

server.run()

This runs on localhost. It has no authentication. No error handling. No tests. No deployment story.

Try deploying this to production for 10,000 employees.

You'll quickly discover:

• How do users authenticate?
• How can it connect securely to data systems
• Where does this run?
• How do we update it?
• What happens when it fails?
• How do we know it's working?
• Who's responsible for it?

This course answers all these questions.

The PMCP Approach

The PMCP SDK and cargo-pmcp toolkit provide:

You focus on business logic. PMCP handles the infrastructure.

What You'll Learn

By the end of this section, you'll understand:

1. Why do we need MCP in the age of LLMs (statistic models vs. symbolic computation)
2. Why MCP over alternatives (custom integrations, RAG, etc.)
3. Why Rust for enterprise (safety, performance, reliability)
4. How to build production-ready servers from day one

Let's start with why MCP beats the alternatives.

Continue to The AI Integration Problem →