The field of AI is undergoing a significant architectural shift. We are moving from standalone AI systems that provide isolated capabilities toward interconnected ecosystems of specialized agents that collaborate to solve complex problems. This evolution mirrors the historical development of human organizations, where specialization and communication allowed for more sophisticated collective capabilities.

As AI systems grow more capable and autonomous, the need for standardized communication mechanisms becomes increasingly critical. Without established protocols, organizations face challenges including:

1. Technical Fragmentation: Teams developing separate integration methods for each agent pairing, leading to duplicated effort and inconsistent implementations.
2. Security Vulnerabilities: Ad-hoc communication systems often lack robust authentication, authorization, and data protection mechanisms.
3. Limited Composability: Without standardized interfaces, combining capabilities from different AI systems becomes prohibitively complex.
4. Governance Challenges: Tracking information flow, maintaining audit trails, and ensuring accountability becomes difficult when agent communication occurs through diverse, non-standardized channels.

AI Agent communication protocols aim to address these challenges by providing structured frameworks that define how agents advertise capabilities, request services, exchange information, and coordinate activities. These protocols serve as the foundational infrastructure upon which sophisticated multi-agent systems can be built.

In this evolving landscape, two significant protocols have emerged as potential industry standards: Model Context Protocol (MCP) developed by Anthropic and Agent-to-Agent (A2A) recently introduced by Google. Each brings a distinct perspective on how AI Agent communication should be structured, secured, and integrated into enterprise workflows.

Understanding the architectural foundations, benefits, limitations, and optimal use cases for each protocol is essential for organizations planning their AI infrastructure investments. This comparative analysis will help technical leaders make informed decisions about which protocol—or combination of protocols—best suits their specific requirements and use cases.

What is MCP?

Model Context Protocol (MCP) represents Anthropic’s approach to establishing a standardized framework for AI Agent communication and operation. At its philosophical core, MCP recognizes that as AI systems grow in complexity and capability, they require a consistent, structured way to interact with external tools, data sources, and services.

MCP emerged from the practical challenges faced by developers building sophisticated AI applications. Without standardization, each team was forced to develop custom integration methods for connecting their AI systems with external capabilities—resulting in duplicated effort, inconsistent implementations, and limited interoperability between systems.

The protocol addresses these challenges by providing a unified method for structuring the context in which AI models operate. It defines clear patterns for how information should be organized, how models should access external resources and tools, and how outputs should be formatted. This standardization allows for better interoperability between different AI systems, regardless of their underlying architecture or training methodology.

Rather than focusing on direct agent-to-agent communication, MCP emphasizes the importance of structured context—ensuring that AI systems have access to the information and capabilities they need in a consistent, well-organized format. This approach treats tools and data sources as extensions of the model’s capabilities, allowing for dynamic composition of functionality without requiring extensive pre-programming.

By providing this standardized interface for context management, MCP aims to reduce ecosystem fragmentation, enable more flexible AI deployments, and facilitate safer, more reliable AI systems that can work together coherently while maintaining alignment with human intentions and values.

MCP Architecture

MCP’s architecture is built around a hierarchical context structure that organizes information and capabilities into clearly defined components. This architecture follows several key design principles that prioritize clarity, security, and flexibility while maintaining clear separation between different types of contextual information.

Core Architectural Components:

1. MCP Host: The “brain” of the system—an AI application using a Large Language Model (LLM) at its core that processes information and makes decisions based on available context. The host is the primary consumer of the capabilities and information provided through the MCP framework.
2. MCP Client: Software components that maintain 1:1 connections with MCP servers. These clients serve as intermediaries between hosts and servers, facilitating standardized communication while abstracting away the complexities of server interactions.
3. MCP Server: Lightweight programs that expose specific capabilities through the standardized protocol. Each server is responsible for a discrete set of functionalities, promoting separation of concerns and allowing for modular composition of capabilities.
4. Local Data Sources: Files, databases, and services on the local machine that MCP servers can securely access. These provide the foundation for contextual information from the immediate environment.
5. Remote Data Sources: External systems available over the internet (typically through APIs) that MCP servers can connect to, expanding the potential information sources available to AI systems.

Figure 1 MCP Architecture (source: Tahir^[1])

Context Structure and Information Flow:

The MCP architecture implements a controlled information flow where context passes through defined pathways. When an MCP Host needs to access external information or capabilities, it connects to appropriate MCP Servers through MCP Clients. The servers then mediate access to various data sources, ensuring that information is properly formatted and permissions are appropriately handled.

This structured flow ensures that all processing occurs within well-defined boundaries, making it easier to track how information moves through the system and maintain security and accountability. The architecture explicitly distinguishes between:

• Tools: Model-controlled actions that allow the AI to perform operations such as fetching data, writing to a database, or interacting with external systems.
• Resources: Application-controlled data such as files, JSON objects, and attachments that can be incorporated into the AI’s context.
• Prompts: User-controlled predefined templates (similar to slash commands in modern IDEs) that provide standardized ways to formulate certain types of requests.

Figure 2 is a sequence diagram showing the information flow between different components in a system that uses MCP to retrieve blog data, specifically SQL-related blog posts, for a user. This type of flow would be useful in a plugin-style AI integration where the AI needs to interact with external data sources via a protocol like MCP but requires explicit user permission and intelligent capability discovery.

Figure 2 MCP Workflow Example (source: Gökhan Ayrancıoğlu^[2])

Protocol Implementation:

MCP is designed to be transport-agnostic, though the initial implementation focuses on HTTP/HTTPS as the primary transport layer. The protocol defines standardized message formats for tool registration, tool invocation, and result handling, ensuring consistent interaction patterns regardless of the specific tools or data sources being accessed.

Recent developments in the protocol have expanded support for remote MCP servers (over Server-Sent Events) and integration with authentication mechanisms like OAuth, making it suitable for enterprise deployments where security and distributed access are essential requirements.

This architecture aims to create a standardized environment for AI processing, where information sources are clearly delineated, tools are discoverable and consistently invocable, and outputs adhere to predictable formats enabling safer multi-agent interactions and clearer accountability.

MCP Benefits

MCP offers several substantial advantages that make it particularly valuable for organizations implementing enterprise-grade AI systems. These benefits directly address common challenges in AI development and deployment, providing tangible improvements in development efficiency, system flexibility, and organizational collaboration.

Ecosystem Standardization and Reduced Fragmentation:

One of MCP’s most significant benefits is the reduction in ecosystem fragmentation. Before standardized protocols, every team building AI applications had to develop custom integrations for connecting their systems with tools and data sources. This resulted in duplicated effort, inconsistent implementations, and limited interoperability.

MCP addresses this challenge by providing a standardized way to connect AI systems with external capabilities. This standardization significantly reduces development overhead and creates a more cohesive AI ecosystem where components can be easily shared and reused. Organizations can develop MCP servers once and leverage them across multiple AI applications, maximizing return on development investments.

Dynamic Composability of Capabilities:

MCP enables dynamic composability of AI systems. Agents and applications can discover and use new tools without pre-programming, allowing for more flexible and adaptable AI deployments. This composability means that organizations can incrementally enhance their AI capabilities by adding new MCP servers without needing to modify existing applications.

For example, a company might initially deploy an AI assistant with access to document search capabilities through an MCP server. Later, they could add financial analysis capabilities by deploying a new MCP server—and the assistant would be able to leverage these capabilities without requiring major modifications to its core implementation.

Enhanced Tool Integration and Context Management:

MCP provides a consistent framework for integrating external tools and capabilities into AI systems. This consistency makes it easier for developers to add new functionalities to their AI applications and for end-users to understand how to interact with those capabilities.

The protocol’s structured approach to context management ensures that models have access to the information they need in a well-organized format. This reduces the risk of context confusion and helps maintain consistent performance across different implementations. The clear separation between different types of contextual information (tools, resources, and prompts) also facilitates better governance and security practices.

Support for Enterprise Collaboration and Workflows:

The protocol aligns well with enterprise organizational structures, where different teams often maintain specialized services and capabilities. Teams can own specific services (such as vector databases, knowledge bases, or analytical tools) and expose them via MCP for other teams to use. This supports organizational separation of concerns while enabling cross-functional collaboration through standardized interfaces.

This alignment with enterprise workflows makes MCP particularly valuable for large organizations with diverse AI initiatives across multiple departments. It provides a common language for AI capabilities while respecting organizational boundaries and governance requirements.

Foundation for Self-Evolving Agent Systems:

MCP enables the creation of self-evolving agents that can grow more capable over time without requiring constant reprogramming. As new tools become available through the MCP registry, agents can discover and incorporate these capabilities dynamically—allowing for continuous improvement without manual intervention.

This foundation for evolving capabilities is especially valuable as organizations move toward more autonomous AI systems that need to adapt to changing requirements and opportunities.

These benefits collectively enable organizations to implement AI systems that are more interoperable, more easily extended, and better integrated into existing enterprise workflows and technology stacks.

MCP Challenges

Despite its numerous advantages, implementing MCP presents several significant challenges that organizations need to carefully consider. Understanding these limitations is essential for realistic planning and effective risk management when adopting the protocol.

Authentication and Security Framework Limitations:

One notable limitation of MCP in its current form is its relatively basic authentication mechanisms. While recent updates have improved OAuth integration, MCP lacks the comprehensive authentication frameworks that are essential for secure enterprise deployments across organizational boundaries.

This limitation becomes particularly significant when implementing MCP in environments where security is a critical concern, especially when AI systems need to access sensitive information or perform operations with potential security implications. Organizations implementing MCP in such environments will need to develop additional security layers to complement the protocol’s native capabilities.

Remote Server Management Complexity:

Although MCP has expanded to support remote MCP servers (over Server-Sent Events), managing these remote connections securely and reliably presents additional complexity. Organizations deploying MCP across distributed environments need to develop strategies for handling connection failures, latency issues, and security considerations.

This distributed architecture introduces potential points of failure that must be carefully managed, especially for mission-critical AI applications. Implementing robust monitoring, error handling, and recovery mechanisms becomes essential when deploying MCP at scale across distributed infrastructures.

Registry Development and Tool Discovery Maturity:

The planned MCP Registry for discovering and verifying MCP servers is still in development. Until this component is fully realized and mature, organizations face challenges in implementing dynamic tool discovery—one of the protocol’s key promised benefits.

Without a robust registry system, organizations must develop interim solutions for tool discovery and verification, potentially limiting the dynamic composition capabilities that make MCP valuable. This gap between the current implementation and the full vision for MCP requires pragmatic planning for organizations adopting the protocol today.

Connection Lifecycle Management:

MCP is still refining how it handles the distinction between stateful (long-lived) and stateless (short-lived) connections. This distinction is important for different types of AI applications, and the current^[3] implementation may not fully address all use cases, particularly those requiring sophisticated state management across extended interaction sessions.

Organizations implementing MCP need to carefully consider their connection lifecycle requirements and may need to develop custom solutions for cases that fall outside the protocol’s current capabilities in this area.

Multi-Agent Coordination Limitations:

While MCP excels at connecting individual AI systems with tools and data, it provides less robust support for direct agent-to-agent communication in multi-agent systems where state is not necessarily shared. This limitation becomes apparent in complex agent ecosystems where multiple autonomous agents need to coordinate their activities directly.

For sophisticated multi-agent architectures, organizations may need to complement MCP with additional protocols or custom solutions to enable effective agent-to-agent communication, particularly when those agents operate across organizational boundaries or vendor environments.

Implementation Complexity and Learning Curve:

Adopting MCP requires investment in understanding and implementing the protocol’s specifications. For organizations with existing AI infrastructure, this may require significant refactoring of current systems to comply with MCP’s structural requirements.

This implementation complexity represents a real cost that must be factored into adoption planning. Organizations should expect to invest in developer training, refactoring existing code, and establishing new development practices aligned with the protocol’s requirements.

These challenges highlight the importance of careful planning when implementing MCP, particularly for organizations with complex security requirements or those building sophisticated multi-agent systems.

MCP Main Use Cases

MCP is particularly well-suited for several key application areas where its structured approach to context management delivers significant value. Understanding these optimal use cases helps organizations identify where MCP can provide the greatest return on implementation investment.

AI-Enhanced Development Environments:

MCP has gained significant traction in AI-enhanced coding environments and integrated development environments (IDEs). Tools like Cursor and Zed leverage MCP to provide developers with AI assistants that have rich access to contextual information, including code repositories, documentation, ticket systems, and development resources.

In these environments, MCP excels at:

• Pulling in relevant code context from the current project
• Accessing GitHub issues, documentation, and APIs
• Enabling interaction with development tools and services
• Maintaining appropriate context during extended coding sessions

The protocol’s standardized approach to context management makes it particularly effective for integrating AI capabilities into development workflows, allowing developers to work with AI assistance that truly understands their project context.

Enterprise Knowledge Management Systems:

MCP provides significant value in enterprise environments where AI needs to access, process, and reason over large volumes of organizational knowledge. The protocol’s clear structure for differentiating between various information sources helps maintain information integrity when AI systems need to reference multiple documents, databases, and knowledge bases simultaneously.

These knowledge management applications benefit from MCP’s ability to:

• Access diverse document repositories with appropriate permissions
• Query enterprise databases while maintaining security boundaries
• Incorporate real-time information from organizational systems
• Maintain clear provenance for information incorporated into analyses

This capability makes MCP ideal for implementing corporate knowledge assistants, document processing systems, and intelligent search applications that need to work across diverse information sources while maintaining appropriate security and governance.

Tool-Augmented Agents and Automated Workflows:

Organizations implementing AI Agents that need to leverage external tools benefit significantly from MCP’s standardized tool interface. Agents can autonomously invoke tools to search the web, query databases, perform calculations, or interact with enterprise systems through a consistent, well-defined interface.

This standardization makes it easier to:

• Expand agent capabilities by adding new tools without changing the agent’s core implementation
• Chain multiple tools together into sophisticated workflows
• Maintain clear audit trails of tool invocations and results
• Implement governance controls around tool access and usage

For example, a research assistant agent might use MCP to access scholarly databases, statistical analysis tools, and citation management systems—combining these capabilities dynamically based on specific research requests.

Domain-Specific AI Applications:

MCP provides an excellent foundation for building domain-specific AI applications that require access to specialized data sources or tools. In fields like finance, healthcare, or legal services, MCP allows developers to create AI systems that can interact with domain-specific resources through a standardized interface.

This standardization reduces the development effort required to build and maintain specialized applications by:

• Providing a consistent pattern for integrating domain-specific tools
• Enabling clear separation between the AI model and domain-specific resources
• Facilitating compliance with domain-specific regulations through structured access controls
• Allowing for modular updates to capabilities as domain requirements evolve

For instance, a healthcare AI assistant might use MCP to access medical terminology databases, electronic health record systems, and clinical decision support tools—all through a consistent interface that maintains appropriate clinical governance.

Self-Evolving Agent Systems:

The protocol enables the creation of self-evolving agents that can grow more capable over time without requiring constant reprogramming. These systems can:

• Dynamically discover new tools via the registry
• Combine MCP with computer vision for UI interactions
• Chain multiple MCP servers for complex workflows (e.g., research → fact-check → report-writing)
• Adapt to new information sources and capabilities as they become available

This capability is particularly valuable for organizations looking to build AI systems that can grow more sophisticated over time, adapting to changing requirements without requiring constant developer intervention.

These use cases highlight MCP’s strengths as a foundational layer for context-aware AI systems, particularly in environments where structured access to diverse information sources and tools is a key requirement.

^[1] https://medium.com/@tahirbalarabe2/what-is-model-context-protocol-mcp-architecture-overview-c75f20ba4498

^[2] https://gokhana.medium.com/what-is-model-context-protocol-mcp-how-does-mcp-work-97d72a11af8a