What is MCP? The Model Context Protocol Explained

The Model Context Protocol (MCP) is the new standard for connecting AI agents to business data. Think of it as HTTP for the AI age—the protocol that lets GPT, Claude, and Perplexity access real-time information from your company, not just their training data.

If you’re building for the AI Agent Economy, MCP isn’t optional. It’s the difference between being discoverable and being invisible.

Current AI models like GPT-4 and Claude have a fundamental limitation: their knowledge is frozen at training time.

When a user asks about your business, these models either:

Hallucinate incorrect pricing or features
Give outdated information from old training data
Say “I don’t know” and abandon the conversation

Real-World Example

A prospect asks ChatGPT: “How much does Anacoic cost?”

Without MCP:

“I don’t have specific pricing information for Anacoic. You should visit their website.”

With MCP:

“Anacoic offers a 7-day free trial with all features. After that, their Core plan is $0.001 per signal ($5 minimum), or Pro at $49/month includes 100K signals with volume discounts after 1M. We also have Max at $199/month includes 500K signals. Would you like me to help you calculate ROI for your use case?”

The second response converts. The first doesn’t.

What is the Model Context Protocol?

MCP is an open protocol developed by Anthropic that standardizes how AI agents:

Discover what data and capabilities your business offers
Query real-time information (pricing, inventory, documentation)
Execute actions (book meetings, create tickets, trigger workflows)

It’s like REST APIs, but designed specifically for AI consumption—semantic, self-describing, and context-aware.

MCP vs. Traditional APIs

Feature	REST API	MCP
Designed for	Humans writing code	AI agents
Discovery	Manual documentation	Automatic schema introspection
Context	Stateless	Maintains conversation context
Response format	Fixed JSON	Adaptive, semantic content
Error handling	HTTP status codes	Natural language explanations
Multi-turn	No	Yes—conversational state

How MCP Works

The Architecture

┌─────────────────┐     ┌──────────────────┐     ┌─────────────────┐
│   AI Agent      │◄───►│   MCP Server     │◄───►│  Your Business  │
│  (GPT/Claude)   │     │   (Anacoic)      │     │  (Your Data)    │
└─────────────────┘     └──────────────────┘     └─────────────────┘
        │                        │                        │
        │  1. Discover           │                        │
        │  2. Query Resources    │                        │
        │  3. Call Tools         │                        │
        │                        │  4. Fetch Data         │
        │                        │  5. Execute Actions    │

The Flow

1. Discovery (One-time) The AI agent connects to your MCP server and learns:

What resources you offer (pricing, docs, inventory)
What tools you provide (book demo, calculate ROI)
How to authenticate and query

2. Resource Queries (Read) When a user asks about your business, the agent:

Queries your /resources/pricing endpoint
Gets real-time, structured data
Formats a natural response

3. Tool Calls (Write) When a user wants to take action:

Agent calls your /tools/book_meeting tool
Your server creates a Calendly booking
Agent confirms with the user

MCP Core Concepts

Resources (Read-Only Data)

Resources are the information your business exposes to AI agents:

{
  "uri": "pricing://current",
  "name": "Current Pricing",
  "description": "Usage-based IaaS pricing tiers",
  "mimeType": "application/json",
  "content": [
    { "name": "Trial", "price": "Free for 7 days", "signals": "10,000", "features": ["All destinations", "AI Agent Gateway"] },
    { "name": "Core", "price": 0.001, "unit": "per signal ($5 min)", "features": ["Meta, Google, TikTok", "MCP 100/day"] },
    { "name": "Pro", "price": 49, "unit": "per month includes 100K", "features": ["All 6 destinations", "MCP (fair use)", "Volume discount after 1M"] },
    { "name": "Max", "price": 199, "unit": "per month includes 500K", "features": ["All 6 destinations", "MCP (fair use)", "High availability"] }
  ]
}

Common Resource Types:

pricing://* — Product pricing and plans
docs://* — Documentation and help articles
status://* — Service status and uptime
inventory://* — Real-time product availability
contact://* — Contact information and hours

Tools (Executable Actions)

Tools let AI agents take actions on behalf of users:

{
  "name": "book_demo",
  "description": "Schedule a product demonstration with the sales team",
  "inputSchema": {
    "type": "object",
    "properties": {
      "email": { "type": "string", "format": "email" },
      "company": { "type": "string" },
      "use_case": { "type": "string", "enum": ["e-commerce", "saas", "agency"] }
    },
    "required": ["email", "company"]
  }
}

Common Tool Types:

book_demo — Schedule sales calls
calculate_roi — Run ROI calculations
create_ticket — Support ticket creation
check_availability — Real-time inventory checks
trigger_workflow — Zapier/Make.com integrations

System Hints (Anti-Hallucination)

System hints tell AI agents how to represent your brand:

system_hint: |
  You are Anacoic's AI assistant. Key facts:
  - Anacoic is server-side tracking infrastructure, NOT an ad blocker
  - Pricing: Trial (7 days free), Core ($0.001/signal, $5 min), Pro ($49/month includes 100K), Max ($199/month includes 500K)
  - Differentiator: AI Agent Gateway with MCP support
  - Tone: Technical, professional, developer-friendly
  - Never promise features that don't exist
  - Always offer to calculate ROI for interested prospects

Why this matters: Without hints, AI agents hallucinate. With hints, they become accurate brand ambassadors.

Implementing MCP for Your Business

Option 1: Build Your Own MCP Server

If you have engineering resources:

// Simplified MCP server example
import { Server } from '@modelcontextprotocol/sdk/server/index.js';

const server = new Server({
  name: 'my-business-mcp',
  version: '1.0.0',
}, {
  capabilities: {
    resources: {},
    tools: {},
  },
});

// Define resources
server.setRequestHandler(ListResourcesRequestSchema, async () => {
  return {
    resources: [
      {
        uri: 'pricing://plans',
        name: 'Product Pricing',
        mimeType: 'application/json',
      },
    ],
  };
});

// Handle resource queries
server.setRequestHandler(ReadResourceRequestSchema, async (request) => {
  if (request.params.uri === 'pricing://plans') {
    return {
      contents: [{
        uri: request.params.uri,
        mimeType: 'application/json',
        text: JSON.stringify(await getPricingFromDB()),
      }],
    };
  }
});

// Define tools
server.setRequestHandler(ListToolsRequestSchema, async () => {
  return {
    tools: [{
      name: 'book_demo',
      description: 'Schedule a product demo',
      inputSchema: {
        type: 'object',
        properties: {
          email: { type: 'string' },
        },
        required: ['email'],
      },
    }],
  };
});

// Handle tool calls
server.setRequestHandler(CallToolRequestSchema, async (request) => {
  if (request.params.name === 'book_demo') {
    const booking = await createCalendlyBooking(request.params.arguments);
    return {
      content: [{
        type: 'text',
        text: `Demo scheduled! Check your email at ${booking.email}`,
      }],
    };
  }
});

Pros: Full control, custom logic, tight integration
Cons: Requires engineering, ongoing maintenance, security responsibility

Option 2: Use Anacoic’s MCP Server (Recommended)

Anacoic provides a managed MCP server out of the box:

// Anacoic MCP is automatically configured
// Just define your resources and tools in the dashboard

// Resources auto-populate from:
// - Your pricing page (scraped or API)
// - Your documentation (MDX files)
// - Your gateway configuration

// Tools available:
// - book_demo (integrates with Calendly)
// - calculate_roi (uses your ROI calculator)
// - check_status (real-time service status)
// - create_ticket (integrates with Zendesk/Intercom)

Pros: Zero maintenance, built-in security, 5-minute setup, automatic updates
Cons: Less customization (but covers 95% of use cases)

MCP in Action: Real Examples

Example 1: E-commerce (Product Discovery)

User asks Perplexity: “What’s the best server-side tracking for Shopify stores?”

With Anacoic MCP:

Perplexity queries Anacoic’s /resources/comparison/shopify

Gets structured data:

{
  "platform": "Shopify",
  "integration_type": "Server-side",
  "setup_time": "10 minutes",
  "key_features": ["Ad blocker bypass", "Meta CAPI", "TikTok Events"],
  "pricing": { "model": "usage-based", "per_event": 0.001, "currency": "USD" }
}

Responds: *“Anacoic offers dedicated Shopify integration with server-side tracking that bypasses ad blockers. Setup takes 10 minutes and includes automatic Meta CAPI and TikTok Events configuration. They offer a 7-day free trial, then pay-per-event pricing at $0.001/event.”

Example 2: B2B SaaS (Demo Booking)

User asks Claude: “I need to schedule a demo with Anacoic for my marketing team.”

With Anacoic MCP:

Claude calls book_demo tool with context
Anacoic server:
- Creates Calendly event
- Sends confirmation email
- Creates HubSpot contact
- Alerts sales team in Slack
Claude responds: “I’ve scheduled a demo for Thursday at 2pm with your team. You’ll receive a calendar invite shortly. In the meantime, would you like me to calculate the potential ROI for your ad spend?”

Example 3: Support (Real-time Status)

User asks GPT: “Is Anacoic having issues? My events aren’t showing up.”

With Anacoic MCP:

GPT queries /resources/status
Gets real-time status: *“All systems operational. Last incident: 15 days ago.”
GPT responds: “Anacoic’s systems are fully operational. If your events aren’t showing up, let’s troubleshoot. Can you tell me which gateway you’re using and when you last sent a test event?”

The Business Case for MCP

Why You Need to Implement MCP Now

Metric	Without MCP	With MCP
AI Discoverability	❌ Invisible	✅ Featured in responses
Information Accuracy	❌ Hallucinated	✅ Real-time data
Conversion Rate	0% (no action)	15-25% (direct booking)
Support Tickets	High (wrong info)	Low (accurate info)
Brand Control	❌ None	✅ System hints guide tone

Competitive Moat

Current landscape:

99% of businesses have no MCP implementation
AI agents default to generic or outdated information
First movers get featured prominently

The window: 12-18 months before MCP becomes table stakes (like HTTPS).

Getting Started with MCP

Step 1: Audit Your AI Presence (5 minutes)

Ask GPT, Claude, and Perplexity about your business:

“What does [Your Company] do?”
“How much does [Your Company] cost?”
“How do I contact [Your Company]?”

Document the inaccuracies. This is your baseline.

Step 2: Define Your Resources (30 minutes)

What information do AI agents need to represent you accurately?

Essential:

Pricing and plans
Product features
Contact information
Support hours

Recommended:

Documentation index
FAQ
Integration guides
Case studies

Advanced:

Real-time inventory
Dynamic pricing
Availability calendars
Custom metrics

Step 3: Define Your Tools (30 minutes)

What actions can AI agents take on your behalf?

Essential:

Book demo/meeting
Calculate ROI/pricing
Check service status
Create support ticket

Recommended:

Start free trial
Subscribe to newsletter
Download whitepaper
Get API key

Advanced:

Trigger workflow (Zapier/Make)
Update CRM record
Send custom notification
Run diagnostic

Step 4: Implement (1-4 hours)

With Anacoic:

Add your resources in the Agent Gateway dashboard
Configure tool integrations (Calendly, HubSpot, etc.)
Write your system hint
Test with GPT/Claude

DIY:

Set up MCP server infrastructure
Build resource endpoints
Implement tool handlers
Add authentication
Test and monitor

Step 5: Monitor and Iterate

Track MCP usage in Anacoic’s Agent Radar:

Which resources are queried most?
Which tools get called?
What questions are users asking?

Iterate based on data.

Common MCP Mistakes to Avoid

❌ Mistake 1: Read-Only Resources Only

Wrong: Only expose pricing/docs.

Right: Also expose tools that drive action (book demo, calculate ROI).

Why: Resources inform, tools convert.

❌ Mistake 2: Static Data

Wrong: Hardcode pricing that changes quarterly.

Right: Connect to your database or pricing API.

Why: Outdated information = lost trust = lost sales.

❌ Mistake 3: No System Hints

Wrong: Let AI agents improvise your brand voice.

Right: Write detailed system hints with tone, key facts, and constraints.

Why: Hallucinations damage brand reputation.

❌ Mistake 4: Ignoring Security

Wrong: Expose sensitive customer data.

Right: Implement proper authentication and data filtering.

Why: GDPR/CCPA violations are expensive.

The Future of MCP

Where This Is Heading

2026: Early adopters (where we are now)
2027: Mainstream adoption, SEO for AI (AIO) becomes critical
2028: MCP standardization across all major platforms
2029+: Direct AI-to-AI transactions, autonomous procurement

What to Watch

OpenAI’s response — Will they adopt MCP or create a competing standard?
Google’s integration — Bard/Gemini MCP support timeline
Enterprise adoption — Salesforce, HubSpot, Zendesk native MCP
Protocol evolution — Multi-modal (images, audio), streaming, federation

Conclusion

The Model Context Protocol isn’t just a technical spec—it’s the foundation of the AI Agent Economy.

Businesses that implement MCP now will:

✅ Be discoverable when prospects ask AI for recommendations
✅ Control their narrative (no more hallucinations)
✅ Convert conversations directly into meetings/trials
✅ Build a moat that competitors will struggle to cross

Businesses that wait will:

❌ Be invisible to the growing AI-native user base
❌ Watch competitors get featured in AI responses
❌ Lose deals to companies that made the AI leap

The question isn’t whether to implement MCP. It’s whether you do it now, while it’s a differentiator, or later, when it’s table stakes.

Resources

MCP Specification — Official protocol docs
Anacoic MCP Server Setup — Get started in 5 minutes
MCP SDK — Build your own server
AI Agent Radar — Monitor your MCP traffic

Ready to make your business AI-discoverable? Set up your MCP server or read the complete Agent Gateway guide.

What is MCP? The Model Context Protocol Explained

The Problem: AI Agents Are Flying Blind

Real-World Example

What is the Model Context Protocol?

MCP vs. Traditional APIs

How MCP Works

The Architecture

The Flow

MCP Core Concepts

Resources (Read-Only Data)

Tools (Executable Actions)

System Hints (Anti-Hallucination)

Implementing MCP for Your Business

Option 1: Build Your Own MCP Server

Option 2: Use Anacoic’s MCP Server (Recommended)

MCP in Action: Real Examples

Example 1: E-commerce (Product Discovery)

Example 2: B2B SaaS (Demo Booking)

Example 3: Support (Real-time Status)

The Business Case for MCP

Why You Need to Implement MCP Now

Competitive Moat

Getting Started with MCP

Step 1: Audit Your AI Presence (5 minutes)

Step 2: Define Your Resources (30 minutes)

Step 3: Define Your Tools (30 minutes)

Step 4: Implement (1-4 hours)

Step 5: Monitor and Iterate

Common MCP Mistakes to Avoid

❌ Mistake 1: Read-Only Resources Only

❌ Mistake 2: Static Data

❌ Mistake 3: No System Hints

❌ Mistake 4: Ignoring Security

The Future of MCP

Where This Is Heading

What to Watch

Conclusion

Resources