Model Control Protocol: The HTTP Moment for AI

Bottom Line: Model Control Protocol (MCP) represents a foundational shift in AI development - similar to how HTTP standardized web communication. This could disrupt current AI service businesses while creating massive opportunities for aggregation layer builders.

What is Model Control Protocol?

MCP acts as a universal connector sitting between:

• 🔧 Tools & Data Sources
• 🤖 LLM Applications
• 💻 Development Environments (like Cursor)

Think of it as the HTTP of AI - a standardized protocol layer that enables seamless connectivity across the AI stack.

The Disruption Potential

Current AI Service Business Model

Many AI service businesses today follow this pattern:

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Customer Need → Custom AI Agents → Multiple LLMs + RAGs + Prompt Flows → Delivered Solution

Example: An “AI-powered SEO service” might involve:

• Multiple AI agents working with different LLMs
• Static RAGs prepared for specific industries
• Custom prompt flows for each customer’s content requirements
• Manual switching between design files, LLM chats, social analytics

Post-MCP Reality

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Customer Need → MCP-Powered Universal Engine → Standardized Output

The Risk: Your $10k ARR AI service could be replicated by a universal MCP-powered engine that handles:

• ✅ Data extraction across sources
• ✅ Content manipulation
• ✅ Output generation (agnostic of LLM/tools)

Opportunity Areas

1. AI Aggregation Layers

The Big Opportunity: Build aggregation layers across industries using MCP

Instead of custom AI agents for each client, create:

• Industry-specific MCP servers that connect to relevant tools/data
• Standardized AI workflows that work across similar use cases
• Plug-and-play solutions for common business problems

2. AI-Driven Sales Focus

High-Potential Vertical: Sales automation and customer engagement

Current state: Lots of custom agentic activities for different industries MCP Opportunity: Abstract backend connectivity so developers can focus on hyper-building rather than customizing agents from scratch

3. Developer Productivity Tools

The Windsurf Moment: We’re entering the “wild west” of AI-powered product building

MCP enables rapid prototyping and iteration across:

• Multi-modal content creation
• Cross-platform integrations
• Real-time data processing

Strategic Implications

For AI Service Providers

Immediate Actions:

1. Evaluate defensibility - What parts of your service can’t be commoditized by MCP?
2. Build proprietary data moats - Unique datasets become more valuable
3. Focus on industry expertise - Domain knowledge becomes the differentiator

For Developers/Founders

Opportunities:

1. MCP Server Development - Build connectors for specific industries/tools
2. Workflow Automation - Create sophisticated multi-step AI processes
3. Integration Platforms - Connect previously incompatible AI tools/services

For Investors

Investment Themes:

1. Infrastructure plays - Companies building MCP tooling/platforms
2. Vertical-specific solutions - Industry-focused MCP implementations
3. Data advantage - Companies with unique, high-quality datasets

Technical Architecture Benefits

Before MCP

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AI App 1 ← Custom Connector → Tool A
AI App 1 ← Custom Connector → Tool B  
AI App 2 ← Custom Connector → Tool A
AI App 2 ← Custom Connector → Tool C

With MCP

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AI Apps ← MCP Protocol → Universal MCP Server ← All Tools/Data

Benefits:

• 🔄 Reusability: One MCP server serves multiple applications
• 🚀 Speed: Faster development and deployment
• 🔧 Maintainability: Centralized tool/data management
• 📈 Scalability: Easy addition of new tools/data sources

Real-World Use Cases

Content Creation Workflow

Traditional Approach:

1. Switch between design tools
2. Manually copy content to LLM chats
3. Analyze social analytics separately
4. Custom post formatting for each platform

MCP-Powered Approach:

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Single Interface → MCP Server → [Design Tools + LLMs + Analytics + Social Platforms] → Formatted Output

Sales Automation

Current: Custom agents for each industry/company MCP Future: Universal sales engine with industry-specific MCP connectors

The Path Forward

For Early Adopters

1. Experiment with existing MCPs - Start with available connectors
2. Identify automation opportunities - What manual workflows can be MCP-ified?
3. Build vertical solutions - Focus on specific industries/use cases

For the Ecosystem

Success Factors:

• Standardization: Consistent MCP implementation across tools
• Security: Robust authentication and access control
• Performance: Low-latency connections for real-time applications
• Documentation: Clear guidelines for MCP server development

Key Takeaways

The Transformation:

• MCP could be as foundational for AI as HTTP was for the web
• Current AI service businesses need to evaluate their defensibility
• Massive opportunity for aggregation layer builders
• Developer productivity could accelerate dramatically

Action Items:

1. AI Service Owners: Assess what parts of your business are MCP-resistant
2. Developers: Start experimenting with MCP implementations
3. Investors: Look for companies building MCP infrastructure and vertical solutions

The Bottom Line: We’re potentially witnessing the emergence of a new protocol layer that could reshape how AI applications are built and deployed. Those who understand and leverage MCP early will have significant advantages in the evolving AI landscape.

What daily use cases are you automating with AI today? How might MCP change your approach? Share your thoughts and experiences.