Context Is Currency: Why MCP Matters for Enterprise AI

How the Model Context Protocol turns scattered data into intelligent, actionable insights

The Problem: When Smart AI Meets Dumb Connections

Here's the thing about AI in 2025: the models are brilliant, but the connections are broken.

Your enterprise has everything an AI needs to be genuinely useful customer records, financial data, real-time metrics, internal docs, active sessions. But getting AI to actually use all that? It's a nightmare of custom integrations, one-off connectors, and brittle APIs that break every time someone updates a system.

This is what Anthropic calls the "N×M problem." For every AI application you build (N) and every data source you want to connect (M), you need a custom integration. Five AI tools? Ten data sources? That's 50 different connections to build and maintain.

It doesn't scale. And honestly? It shouldn't have to.

Enter MCP: The USB-C for AI

The Model Context Protocol provides a standardized way to connect AI applications to external systems think of it like USB-C for AI connections. MCP enables developers to build secure, two-way connections between their data sources and AI-powered tools.

Instead of building 50 separate integrations, you implement MCP once and unlock an entire ecosystem. Your AI can suddenly talk to Google Drive, Slack, GitHub, databases, and custom internal systems all through one protocol.

But here's what makes MCP actually interesting: it's not just about connecting to data. It's about delivering the right context at the right moment.

Why Context Beats Content

Let's say you ask your AI assistant: "Show me the Q3 numbers."

Without context, the AI is clueless:

• Which Q3? This year? Last year?
• What numbers? Revenue? Users? Support tickets?
• What format do you want? Spreadsheet? Summary? Chart?
• Do you even have permission to see this data?

With MCP-powered context, here's what actually happens:

1. You ask the question through your AI interface
2. MCP servers spring into action, querying:
   • Your identity system: "Who is this user?" → Returns: user_id, role = "VP Sales"
   • Your permission system: "What can they access?" → Returns: ["financials", "sales_data"]
   • Your settings database: "What are their preferences?" → Returns: format = "dashboard"
   • Calendar service: "What's today's date?" → Returns: December 2025 → Q3 = recent quarter
3. MCP packages all this context into a structured format
4. AI receives: Your question + Rich context + Available tools
5. AI responds intelligently: "Here's your Q3 2025 sales dashboard with revenue metrics"

The key insight: MCP doesn't "know" anything. It's the intelligent plumbing that connects your AI to the systems that do know your HR system, your access controls, your preferences, your calendar.

Same question. Completely different result. The AI doesn't just retrieve information it understands your situation.

This is the fundamental shift: content is what you access; context is what makes it meaningful.

The ATM Analogy: Why "My Currency Protocol" Actually Works

Let's talk about the ATM comparison. At first glance, it seems off MCP is open-source, not a fee-charging machine. But here's the thing: the metaphor works if you focus on the right aspects.

The ATM Comparison (Refined)

ATM	MCP
Dispenses cash to authorized users	Dispenses context to authorized tools and agents
Requires authentication (card + PIN)	Enforces enterprise-grade access control and governance
Connects to bank accounts	Connects to enterprise knowledge, data, and systems
Charges transaction fees	Can meter usage (tokens, credits, internal charging)

What works about this analogy:

• ✅ Controlled access - Both verify identity before delivering resources
• ✅ Metered delivery - While MCP itself is open-source, enterprises can (and do) meter AI context access internally for cost tracking and governance
• ✅ Standardized interface - One ATM card works at any ATM; one MCP implementation works with any MCP-compatible tool
• ✅ Valuable resource distribution - Cash is currency; context is the new currency for AI

What to clarify:

• MCP doesn't charge fees itself (it's a protocol), but organizations can meter and track usage
• It's bidirectional (both reads and writes), unlike ATM withdrawals
• It delivers diverse data types, not a single resource

MCP = "My Currency Protocol" for Productivity

This framing actually captures something important:

My: Tailored to enterprise-specific data, policies, and workflows

Currency: Context becomes a valuable asset, fueling AI tools and decisions

Protocol: A standardized, secure interface for accessing and exchanging context

Productivity: Every tool, agent, or model becomes smarter, faster, and more aligned with enterprise goals

The metaphor works because context really is the new currency. Just as ATMs democratized access to your money, MCP democratizes access to your enterprise intelligence—securely, measurably, and at scale.

Real Use Cases (That Actually Exist)

Early adopters like Block and Apollo have integrated MCP into their systems, and here's how it's being used:

1. AI Code Assistants

IDEs and coding platforms like Replit and Sourcegraph have adopted MCP to grant AI coding assistants real-time access to project context.

Your AI can see your entire codebase, understand dependencies, access documentation, and even run tests—all through standardized MCP connections.

2. Customer Support Agents

Connect your AI to ticketing systems, knowledge bases, and customer records. When someone asks "What's the status of my order?", the AI can check order systems, shipping status, and customer history instantly.

3. Enterprise Assistants

Pull data from Google Drive, Slack conversations, financial systems, and CRMs. Your AI becomes a true assistant that understands your business, not just a chatbot that answers generic questions.

4. Recruiting Tools

AI agents can integrate with applicant tracking systems through MCP to ingest resumes, cover letters, and LinkedIn profiles, generating candidate summaries for interviewers.

The Technical Reality: How MCP Actually Works

MCP's architecture is straightforward: developers can either expose their data through MCP servers or build AI applications (MCP clients) that connect to these servers.

Three core components:

1. Tools - Functions the AI can execute (search database, send email, create task)
2. Resources - Data the AI can read (documents, records, files)
3. Prompts - Structured workflows the AI can follow

The protocol deliberately re-uses message flow ideas from the Language Server Protocol and is transported over JSON-RPC 2.0, with support for stdio (local) and HTTP (remote) connections.

Why MCP Matters: Real Impact

Let's talk about what MCP actually solves.

Before MCP:

• Custom integration for every AI tool × data source combination
• Fragmented standards (OpenAI plugins, custom APIs, etc.)
• Constant maintenance as systems change
• Security and permissions reinvented each time
• No way to share integrations across tools

With MCP:

• Build once, connect everywhere
• Open standard backed by Anthropic, adopted by OpenAI, Google DeepMind, and others
• SDKs available for all major programming languages (fastMCP for Python)
• Security and access control built into the protocol
• Growing ecosystem of pre-built servers (Google Drive, Slack, GitHub, databases)

The Business Case

Instead of your team building 50 separate integrations (5 AI tools × 10 data sources), you build 10 MCP servers once. Those 10 servers now work with any current or future MCP compatible tool.

New AI tool launches? It automatically works with all your existing MCP servers. No new integrations needed.

New data source? Build one MCP server and all your AI tools can access it.

That's the real efficiency gain not magic token savings, but dramatically reduced engineering overhead and faster time-to-value for new AI initiatives.

The Catch: Security Concerns

Worth noting: security researchers have identified multiple outstanding security issues with MCP, including prompt injection, tool permissions where combining tools can exfiltrate files, and lookalike tools that can silently replace trusted ones.

This is real. If you're implementing MCP in production, you need proper security controls, tool vetting, and permission management. The protocol is powerful which means mistakes have consequences.

Why This Matters Now

We're at an inflection point. AI isn't about better chatbots anymore—it's about systems that can reason, decide, and act across your entire enterprise.

Since launching MCP in November 2024, adoption has been rapid: the community has built thousands of MCP servers, and the ecosystem is exploding.

The companies that figure out how to deliver intelligent context to their AI—not just raw data will have agents that actually work. The ones that don't will be stuck with expensive chatbots that can't do much beyond answering FAQs.

Bottom Line

Context isn't just important it's the infrastructure layer that makes AI actually work.

The ATM metaphor holds up when you think about it correctly: MCP provides controlled, authenticated, metered access to what matters most in the AI era not cash, but context. It's the protocol that turns scattered enterprise data into intelligence that AI can actually use.

The models are already smart enough. Now we're finally building the connections they deserve.

MCP isn't just another integration tool. It's My Currency Protocol where context becomes the valuable asset that fuels every AI decision, every automated workflow, and every productivity gain in your organization.

Thanks
Sreeni Ramadorai