Standardizing AI-to-System Integration

Model Context Protocol

Part 1: Architecture, Implementation, and Protocol Analysis

This is the first in a three-part series examining Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocols. Part 1 focuses on MCP fundamentals and architecture, Part 2 will examine A2A in detail, and Part 3 will provide comparative analysis of both protocols.

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Executive Summary

Model Context Protocol (MCP) represents a significant shift in how AI systems interact with external tools and data sources. Rather than creating new functionality, MCP standardizes existing capabilities through a universal protocol, eliminating the need for bespoke integrations between AI assistants and external systems.

Key Finding: MCP didn't change the underlying APIs—it standardized how AI systems discover, connect to, and communicate with them.

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Introduction: The Standardization Imperative

When Anthropic introduced Model Context Protocol (MCP), the initial reaction from many developers was familiar: "Isn't this just another API wrapper?" This skepticism, while understandable, misses the fundamental value proposition of MCP.

MCP is an open standard that enables seamless integration between AI assistants and external data sources and tools. It standardizes how AI models connect with external systems, replacing fragmented integrations with a single protocol. But to understand why this matters, consider the alternative.

The Restaurant Analogy: Before and After MCP

Scenario 1: The Patchwork Integration Era

Imagine a restaurant where Claude, GPT-4, and Gemini each require separate entrances, custom menus, and dedicated translators to order the same dish. Each AI system needs:

• Separate entry points: Custom authentication and connection methods

• Unique menu formats: Different schema definitions for the same tools

• Dedicated translators: Bespoke wrapper code for each AI-tool combination

• Friction for changes: Adding new capabilities requires modifying every integration

This represents the pre-MCP world: N hosts × M services = N×M bespoke solutions.

Scenario 2: The MCP Standardization

Now imagine the same restaurant with a single entrance, universal menu, and common service protocol. Every AI system can:

• Use one door: Standardized connection and authentication

• Read the same menu: Consistent tool schemas and discovery

• Order through one system: Universal JSON-RPC communication

• Add services seamlessly: New tools work with all existing AI systems

This is MCP's value proposition: M servers, reusable by every AI host.

Critical Insight: The kitchen (underlying APIs) remains unchanged. MCP standardizes the front-of-house experience—discovery, authentication, and communication.

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Architecture Deep Dive

Core Components

MCP defines seven fundamental concepts that comprise the protocol:

1. Core Architecture

The foundation follows a client-server model:

• Hosts: LLM applications (Claude, Cursor, etc.)

• Clients: Maintain 1:1 connections with servers within host applications

• Servers: Provide context, tools, and prompts to clients

2. Resources

Structured, read-only data streams exposed by servers. Resources provide context similar to RAG systems but with standardized access and real-time updates.

3. Tools

Executable functions that servers expose to AI models. Each tool includes:

• Name and description

• Input schema validation

• Output format specification

• Security and access controls

4. Prompts

Reusable instruction templates with placeholder support. Prompts enable:

• Consistent task framing

• Workflow automation

• Multi-step process standardization

5. Sampling

A mechanism allowing servers to request LLM completions through clients, enabling:

• Human-in-the-loop workflows

• Privacy-preserving agentic operations

• Secure model access from external systems

6. Roots

Security boundaries that define server access scope. Roots provide:

• Namespace isolation

• Resource access control

• Privacy enforcement

7. Transports

Communication protocols between clients and servers:

• stdio: Local process communication

• HTTP/Streamable HTTP: Remote API communication

• WebSocket: Real-time bidirectional communication

Protocol Flow

1. Initialization: Client connects to server, negotiating capabilities and roots

2. Discovery: Client queries available tools, resources, and prompts

3. Context Augmentation: Resources and prompts enrich model context

4. Execution: Models invoke tools and access resources within defined boundaries

5. Sampling: Servers can request model completions when needed

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Protocol Analysis: MCP and TCP Similarities

Connection Establishment Patterns

A striking similarity emerges when comparing MCP's initialization sequence to TCP's three-way handshake:

TCP Handshake:

Client → Server: SYN (synchronize)

Server → Client: SYN-ACK (acknowledge + synchronize)

Client → Server: ACK (acknowledge)

MCP Initialization:

Client → Server: initialize (capabilities + version)

Server → Client: initialize response (server capabilities)

Client → Server: initialized (ready confirmation)

Both protocols solve the fundamental "split-brain" problem—ensuring both parties have consistent understanding of connection state and capabilities.

Key Architectural Parallels

Critical Differences

While the initialization patterns are similar, the protocols serve different layers:

• TCP: Transport layer reliability and flow control

• MCP: Application layer capability negotiation and service discovery

Session Termination: A Design Trade-off

Unlike TCP's graceful four-way termination, MCP relies on transport-level connection closure with no specific shutdown messages. This design choice prioritizes simplicity over reliability but introduces potential issues:

Risks:

• Orphaned server sessions consuming resources

• Incomplete cleanup of allocated resources

• Connection pool exhaustion

• Inconsistent state between client and server

Mitigation Strategies:

• Server-side timeout mechanisms

• Heartbeat/ping protocols (MCP includes ping utilities)

• Resource cleanup on connection loss

• Stateless server design where possible

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Current Landscape and Adoption

Competitive Position

MCP occupies a unique position in the AI tooling ecosystem. While not facing direct protocol competition, several frameworks address adjacent concerns:

Industry Momentum

Several factors indicate growing MCP adoption:

1. Vendor Neutrality: No single company controls the specification

2. Host Support: Claude, Cursor, and other major platforms integrate MCP

3. Ecosystem Growth: Increasing number of MCP servers and tools

4. Enterprise Interest: Security and reliability improvements for production use

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Future Roadmap and Development

Based on the official GitHub roadmap, MCP development focuses on four key areas:

1. Validation and Compliance

• Open-source reference clients demonstrating best practices

• Automated compliance test suites for self-verification

• Standardized validation tools for implementations

2. Discovery and Registry

• Central MCP Registry API for server discovery

• Standardized metadata formats for marketplaces

• Automated server cataloging and search capabilities

3. Agent Enhancement

• Support for agent graphs and multi-agent topologies

• Refined human-in-the-loop workflows

• Fine-grained permissions and user interaction patterns

4. Technical Expansion

• Multimodal support beyond text and images

• Chunked bidirectional streaming for real-time experiences

• Enhanced security and privacy controls

5. Governance Evolution

• Community-led development model

• Transparent contribution and review processes

• Potential formal standards body recognition

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Let’s Actually Do Something

To bring this full circle and provide some hands-on experience I’ve created two GitHub repositories, one that provides everything you need to install and run a local MCP server that you can then add to different clients to get a real world understanding of what makes this powerful. The other installation type is leveraging the same MCP server but rather than running it locally the server itself is hosted in AWS and publicly accessible for testing.

🔧 Option 1: Install the MCP Server Locally

If you want to run the full server locally, this is the setup:

✅ Requirements

• Node.js 18+ – Install Node

• Git – Install Git

• An MCP-compatible AI assistant:

  • Claude Desktop

  • Cursor

  • Windsurf (Beta)

📥 Clone and Build

git clone <https://github.com/dp-pcs/Trilogy-AI-CoE-MCP.git>
cd Trilogy-AI-CoE-MCP
npm install
cp env.example .env
npm run build

💡 You can customize the Substack feed in .env if needed.

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🔍 Explore with the MCP Inspector (Optional but Powerful)

Before connecting an AI assistant, you can use the MCP Inspector, a browser-based developer tool that helps you explore and debug your server.

npx @modelcontextprotocol/inspector node dist/index.js

Then open your browser to:

http://localhost:5173

Once it loads, you’ll see:

• Tools tab: Test tools like list_articles, read_article, etc.

• Resources tab: (if any are defined)

• Prompts tab: (if used in your project)

• Notifications panel: Live logs and server messages

Try this:

• Click on list_articles

• Enter { "limit": 2 } as input

• Click “Call Tool”

• Check the JSON response

• Try invalid input (like { "limit": "banana" }) to see error handling

✅ The Inspector gives you confidence your tools are working

before

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🔗 Connect an Assistant (Claude or Cursor)

Add this config to your AI assistant settings:

{
  "mcpServers": {
    "trilogy-ai-coe": {
      "command": "node",
      "args": ["/FULL/PATH/TO/Trilogy-AI-CoE-MCP/dist/index.js"],
      "env": {
        "SUBSTACK_FEED_URL": "<https://trilogyai.substack.com>"
      }
    }
  }
}

💡 Replace the path above with your actual install directory.

Run echo $(pwd)/dist/index.js to get the full absolute path.

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🧪 Test Locally in Claude/Cursor

Once the server is running and connected:

• “List the latest articles from the AI CoE”

• “Show me all authors”

• “What topics are covered?”

• “Read the article about AI strategy”

These queries trigger your tools through the MCP protocol.

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🌍 Option 2: Connect to the Hosted MCP Server (No Setup Required)

If you just want to try things out without hosting locally, use the remote MCP server I’ve deployed to AWS.

✅ What You Need

• Node.js 18+ and Git

• Claude or Cursor (ChatGPT support in beta)

📁 Clone the Remote Client

git clone <https://github.com/dp-pcs/Trilogy-AI-CoE-MCP-Remote.git>
cd Trilogy-AI-CoE-MCP-Remote
npm install

This sets up a bridge client that connects to:

🔗 Live Server → https://ai-coe-mcp.latentgenius.ai

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🧩 Configure Your AI Assistant

Claude:

{
  "mcpServers": {
    "trilogy-ai-coe": {
      "command": "node",
      "args": ["/FULL/PATH/TO/mcp-remote-client.js"]
    }
  }
}

Cursor:

{
  "mcpServers": {
    "trilogy-ai-coe": {
      "command": "node",
      "args": ["/FULL/PATH/TO/mcp-remote-client.js"]
    }
  }
}

ChatGPT (Beta):

• Settings → Connectors → Add MCP Server

  • Name: Trilogy AI CoE MCP

  • URL: https://ai-coe-mcp.latentgenius.ai/mcp

  • Auth: None

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🔍 Try These Prompts

Once connected, test it out:

Search for articles about agentic frameworks

Show me the 5 most recent articles

Who are the authors at the Trilogy AI Center of Excellence

These activate the following tools:

• search

• list_recent

• fetch

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📡 Architecture Overview

Claude ⇄ Local Client ⇄ 🌐 Remote Server

Cursor ⇄ Local Client ⇄ 🌐 Remote Server

ChatGPT ⇄ 🌐 Remote Server (Direct)

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🧯 Troubleshooting

Problem

Fix

No tools / “server not found”

Check full path, restart the assistant

Module or permission errors

Re-run npm install, check Node version, use chmod +x

Server not responding

curl https://ai-coe-mcp.latentgenius.ai/health

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🧠 Want to Host Your Own Remote Server?

The server you’re connecting to is open source. You can host your own using:

• AWS, Railway, Render, Heroku, or Cloud Run

• Docker or traditional VPS

• Custom domains and SSL

⚙️Read the full deployment guide →

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🚀 Ready to Experiment

Use this system for:

• ✅ Local dev and debugging

• ✅ Multi-assistant integration

• ✅ Deploying production-grade AI tools

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Technical Recommendations

For MCP Server Developers

1. Connection Management: Implement heartbeat mechanisms and aggressive timeouts

2. State Design: Prefer stateless architectures where possible

3. Resource Cleanup: Monitor and clean up orphaned connections

4. Error Boundaries: Implement graceful degradation for partial failures

5. Logging: Comprehensive logging for debugging and monitoring

For MCP Host Implementers

1. Capability Negotiation: Gracefully handle version and feature mismatches

2. Timeout Handling: Implement reasonable defaults with override capabilities

3. User Experience: Provide clear feedback on connection status and errors

4. Security: Validate all server responses and sanitize user inputs

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Conclusion

Model Context Protocol represents a maturation point in AI tooling infrastructure. By standardizing the communication layer between AI systems and external tools, MCP eliminates the exponential complexity of N×M custom integrations while preserving the flexibility and power of existing APIs.

The protocol's similarity to established networking protocols like TCP suggests a thoughtful approach to distributed systems design, though some trade-offs (particularly in session management) may require attention as adoption scales.

As the AI ecosystem continues to evolve, MCP's vendor-neutral approach and focus on interoperability position it well to become the standard protocol for AI-to-system integration. The growing ecosystem of tools, servers, and host implementations indicates strong momentum toward widespread adoption.

Looking Ahead: Part 2 of this series will examine Google's Agent-to-Agent (A2A) protocol, exploring how it complements MCP's system integration focus with standardized agent-to-agent communication. Part 3 will provide a comprehensive comparative analysis of both protocols and their combined impact on the AI tooling landscape.

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References and Further Reading

• MCP Official Specification

• GitHub Repository and Changelog

• Trilogy AI CoE MCP Implementation

• MCP Inspector Development Tool

• MCP Infographic

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Author: David Proctor | Published: June 13, 2025 | Series: AI Protocol Analysis (MCP & A2A) (Part 1 of 3)