MCP (Model Context Protocol): What It Is and Why It Matters

Model Context Protocol (MCP) is an open standard for connecting AI models to external data sources and tools. Instead of building custom integrations for every AI + tool combination, MCP provides a universal interface that works across models and applications.

Think of it like USB for AI. Before USB, every device needed a different cable. MCP does the same thing for AI integrations.

The Problem MCP Solves

Today, connecting AI to your tools is painful:

• Every integration is custom - Connect GPT to Salesforce? Build an integration. Connect Claude to Salesforce? Build another one.
• Data access is fragmented - Each app has different APIs, auth methods, and data formats.
• Context is limited - Models only see what you explicitly give them.
• Maintenance is endless - APIs change, integrations break, you're always fixing.

The result: AI applications are islands, disconnected from the systems where your real data lives.

What MCP Does

MCP standardizes how AI models:

1. Discover available tools and data sources
2. Authenticate to external systems
3. Read context from connected sources
4. Execute actions through tools
5. Write results back to systems

With MCP, you build one connection to a data source, and any MCP-compatible model can use it.

How MCP Works

Architecture

Your AI Application
        ↓
   MCP Client
        ↓
   MCP Protocol
        ↓
   MCP Server
        ↓
 External System
   (CRM, Database, API)

Core Components

MCP Client Lives in the AI application. Handles:

• Connecting to MCP servers
• Sending requests
• Processing responses

MCP Server Connects to external systems. Provides:

• Available resources (data the model can read)
• Available tools (actions the model can take)
• Authentication management

MCP Protocol The standard interface between clients and servers. Defines:

• Message formats
• Resource types
• Tool specifications
• Error handling

Message Flow

1. Discovery: Client asks server what resources and tools are available
2. Context: Client requests data from resources
3. Action: Client calls tools to execute actions
4. Response: Server returns results to client

MCP Resources

Resources are data sources the model can read. Examples:

• Files in a directory
• Rows in a database
• Records in a CRM
• Messages in a conversation
• Documents in a knowledge base

Resources have:

• URI: Unique identifier (e.g., file:///path/to/doc.md)
• MIME type: Content format (e.g., text/markdown)
• Content: The actual data

MCP Tools

Tools are actions the model can take. Examples:

• Send an email
• Create a CRM record
• Query a database
• Call an API
• Write a file

Tools have:

• Name: What it's called (e.g., send_email)
• Description: What it does (used by the model to decide when to use it)
• Parameters: Required inputs with types and descriptions
• Response: What it returns

Why MCP Matters

For Developers

Build once, use everywhere An MCP server for Salesforce works with any MCP-compatible AI application—Claude, GPT, Llama, anything.

Faster development Instead of building custom integrations, you use existing MCP servers or build to a standard spec.

Better maintenance Updates to an MCP server improve all applications using it.

For Businesses

More capable AI Models can access your actual business data, not just what's in their training.

Composable AI systems Swap models without rebuilding integrations. Add new data sources without changing your AI application.

Reduced vendor lock-in Your integrations work across AI providers.

For the AI Ecosystem

Interoperability Different AI systems can work together through a common protocol.

Accelerated innovation Developers focus on value-add instead of integration plumbing.

Trust and security Standardized protocols enable better security auditing and controls.

MCP in Practice

Example: Sales AI with MCP

Without MCP:

Custom integration to CRM
Custom integration to email
Custom integration to calendar
Custom integration to enrichment API
Each one is unique code to maintain

With MCP:

MCP server for CRM
MCP server for email
MCP server for calendar
MCP server for enrichment
Any MCP client can use all of them

Building an MCP Server

Here's a simplified example of an MCP server that provides CRM data:

from mcp import Server, Resource, Tool

server = Server("crm-server")

@server.resource("crm://contacts/{contact_id}")
async def get_contact(contact_id: str):
    contact = crm.get_contact(contact_id)
    return Resource(
        uri=f"crm://contacts/{contact_id}",
        mime_type="application/json",
        content=json.dumps(contact)
    )

@server.tool("create_contact")
async def create_contact(name: str, email: str, company: str):
    """Create a new contact in the CRM."""
    result = crm.create(name=name, email=email, company=company)
    return {"id": result.id, "created": True}

server.run()

Using an MCP Client

from mcp import Client

client = Client()
client.connect("localhost:3000")  # Connect to CRM server

# Get a resource
contact = await client.get_resource("crm://contacts/12345")

# Use a tool
result = await client.call_tool("create_contact", {
    "name": "Jane Smith",
    "email": "jane@company.com",
    "company": "Acme Corp"
})

Current State of MCP

MCP is still early but gaining traction:

What exists:

• Open specification published by Anthropic
• SDKs for Python and TypeScript
• Growing library of community MCP servers
• Integration with Claude Desktop and other tools

What's coming:

• More AI providers adopting MCP
• Enterprise-grade MCP servers for major platforms
• Security and governance frameworks
• Ecosystem of commercial MCP providers

MCP vs Alternatives

vs Custom Integrations

MCP is more standardized. Custom integrations are more flexible for unique requirements but harder to maintain.

vs Function Calling

Function calling is model-specific (OpenAI functions, Claude tools). MCP provides a universal protocol across models.

vs LangChain/LlamaIndex

These are frameworks that can use MCP servers. MCP is the protocol; LangChain is a framework that speaks the protocol.

Getting Started with MCP

If you're interested in MCP:

1. Read the spec: modelcontextprotocol.io
2. Try existing servers: GitHub has many open-source MCP servers
3. Build a simple server: Start with a basic resource provider
4. Integrate with your AI: Use Claude Desktop or an MCP-compatible framework

The Bottom Line

MCP is infrastructure. Like HTTP or SQL, it's not exciting on its own—it's what you build on top of it that matters.

For AI to fulfill its potential, it needs to connect to the systems where real work happens. MCP is the emerging standard that makes those connections possible at scale.

Early adoption means you're building on standards that will define the next decade of AI development.

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