MCP: The Universal Connector Everyone's Talking About

Model Context Protocol (MCP) is having a moment right now – and for good reason. Introduced by Anthropic in late 2024, MCP serves a similar function to what HTTP did for web communications: creating a standardized way for AI models to plug into virtually any external system.

MCP specifically lets AI models:

• Discover available tools and data sources

• Request access to external information

• Execute actions across system boundaries

• Maintain context throughout interactions

But here's the critical insight: MCP only defines how information gets exchanged, not what an AI system does with that information once received. That's where agent intelligence capabilities come into play.

The Four Levels of Agent Intelligence

Having a standardized communication protocol is great, but it doesn't magically make your AI system smarter. That requires entirely different architectural approaches. Here's my breakdown of the intelligence spectrum for AI agents:

1. Simple Reflex Systems

These basic if-this-then-that systems operate without memory or context. They follow fixed rules and break when encountering anything unexpected.

Real-world example: A log parser that transforms data using predefined templates but completely fails when log formats change slightly.

2. Memory Systems

These maintain state beyond immediate inputs, creating more coherent behavior over time – the AI equivalent of remembering what happened five minutes ago.

Real-world example: A system that processes sequential security logs and recognizes when events from earlier entries relate to current ones, maintaining session context throughout.

3. Modeling Systems

Now we're getting sophisticated! These agents have internal models of their environment and can simulate "what if" scenarios, optimizing toward goals instead of just following rules.

Real-world example: A security analysis system that spots attack patterns by connecting seemingly unrelated events across different systems – something invisible when looking at individual logs.

4. Self-Learning Systems

The most advanced category – these agents adapt without human supervision, continuously improving through experience.

Real-world example: A log normalization system that automatically adjusts to vendor format changes by detecting patterns in new logs and modifying its transformation rules accordingly.

When Companies Get It Wrong

Here's a pattern I see all the time: A company invests heavily in MCP integration across their systems but completely neglects to build the right intelligence capabilities for their specific challenges. It's like building a perfect highway system with no cars smart enough to drive on it.

The communication infrastructure works flawlessly, but they've missed the intelligence needed to actually do anything useful with all that connected data. This mismatch leads to disappointing results despite significant investment.

Where MCP Fits in the Agentic Architecture

MCP isn't an "agent framework" – it's a standardized integration layer. Think of it as the plumbing that connects an AI agent to the outside world in a structured manner.

Without something like MCP, every time an agent needs to interact with external systems – whether fetching a file, querying a database, or calling an API – developers have to wire up custom integrations. That's like building a robot but having to custom-craft each finger to grasp different objects – tedious and not scalable.

MCP complements agent orchestration tools like LangChain, LangGraph, or CrewAI by providing a unified "toolbox" from which AI agents can invoke external actions. Instead of replacing orchestration (which determines when and why an agent uses a tool), MCP defines how these tools are called and information exchanged.

MCP Servers: The Building Blocks of AI Integration

One critical component of the MCP ecosystem is the concept of MCP servers. These are lightweight servers that expose specific functionalities to AI models through the standardized MCP protocol.

MCP servers act as specialized connectors between AI systems and specific data sources or tools. For example, there are pre-built MCP servers for services like Google Drive, Slack, GitHub, databases like Postgres, and web browsers. Each server implements the standardized protocol but encapsulates all the complexity of interacting with a specific system.

This architecture creates powerful possibilities:

1. Plug-and-Play Functionality: AI models can instantly discover and use any MCP server without custom coding or integration work.

2. Extensibility: Developers can build new MCP servers for any tool or data source, expanding the ecosystem of available capabilities.

3. Composability: Multiple MCP servers can be combined to create complex workflows that span different systems.

The beauty of this approach is that MCP servers separate concerns perfectly: the AI agent doesn't need to know how to authenticate with Slack or parse a database response – the MCP server handles all that complexity. This allows AI engineers to focus on building better intelligence into their models rather than worrying about integration details.

Architectural Principles in Practice: The OCSF Universal Translator

Security log standardization through the Open Cybersecurity Schema Framework (OCSF) perfectly illustrates how these principles come together in practice.

The Challenge

Security tools from diverse vendors generate logs in different formats, requiring different levels of intelligence to standardize – from simple pattern matching for consistent logs, to contextual understanding for varying formats, to semantic modeling for different terminology, to adaptive learning for evolving formats.

The Solution: The Brain Layer of Our OCSF Mapping Architecture

The diagram illustrates the brain layer of our OCSF mapping application with MCP integration:

As shown in the diagram, the architecture consists of:

1. Cross-System Communication (MCP Layer): At the top, the MCP layer provides standardized connectivity to diverse log sources (cloud services, firewalls, endpoints, applications)

2. Intelligence Components in the Brain Layer:

   • Pattern Recognition: For handling simple, consistent log formats (Level 1)

   • Semantic Understanding: For connecting related concepts across different terminologies (Level 2-3)

   • Contextual Processing: For maintaining awareness of relationships between events (Level 2)

   • Adaptive System: For evolving with changing log formats over time (Level 4)

3. Integration with Security Operations: The standardized output connects seamlessly with detection, analytics, and alerting systems

What's powerful about this approach is how it separates the communication protocol (MCP) from the intelligence capabilities within the brain layer. While this diagram focuses on the brain layer components, our complete brain-muscle layer architecture (which adds high-performance execution capabilities) is detailed in our self-adaptive system blog.

This design can leverage MCP-compatible tools like Claude, OpenAI, and Gemini to assist with particular intelligence tasks where appropriate, while maintaining the specialized processing needed for enterprise-scale log standardization.

New Possibilities From This Architectural Approach

When we separate communication protocols from intelligence capabilities, several powerful possibilities emerge:

1. Specialized Components in Ecosystems

Different systems can focus on what they do best – translation, detection, or response – while communicating seamlessly through standardized protocols.

2. Right-Sized Intelligence

Rather than defaulting to the most advanced (and expensive) approach for every problem, organizations can apply the right intelligence level to each specific challenge.

3. Multi-Step, Cross-System Workflows

AI assistants can coordinate complex tasks across multiple systems – from checking calendars to booking venues to sending emails – through a unified communication layer.

4. Collaborating Agent Networks

Specialized AI agents for research, planning, and execution can work together through shared protocols, each optimized for its specific cognitive task.

Conclusion: The Power of Architectural Clarity

The distinction between communication protocols and intelligence capabilities isn't merely academic—it shapes how we build effective AI systems:

• Protocols Enable Connection: MCP defines how AI systems exchange information, creating standardized connectivity

• Intelligence Enables Action: Different intelligence levels define what AI systems can do with that information

• Separation Creates Flexibility: Keeping these layers distinct allows each to evolve independently

By implementing these architectural principles, AI engineers can create more effective systems that match solutions to problems, building the intelligent, interconnected ecosystems that will define the future of enterprise AI.