From Static to Stateful: Revolutionising AI Communication with the MCP Protocol

MCP standardises how AI applications communicate with external systems using JSON‑RPC 2.0 over stateful, bidirectional connections. Unlike standard JSON‑RPC, which treats every request independently, MCP augments the protocol by embedding session tokens and context IDs into each message.

This enhancement provides state, enabling advanced, multi‑step interactions and dynamic module loading at runtime. Such a stateful design is essential for modern, agile AI systems.

Imagine an AI‑powered payments system that dynamically integrates a new fraud detection module during operation. MCP allows the system to load this module on the fly without a full redeployment, provided the server supports dynamic module loading.

In this post, we'll explore the MCP protocol, its core components, and how it compares to traditional REST and GraphQL APIs.

Core Components

Advantages:

• Flexibility & Modularity: The clear separation into client, server, and protocol components enables dynamic integration and iterative enhancements.
• Advanced Capabilities: The stateful design supports multi‑step operations and real‑time context tracking.

Disadvantages:

• Increased Complexity: The extra architectural layers and stateful interactions require careful design and a steeper learning curve.
• Implementation Overhead: The need for sophisticated session management introduces additional development overhead, though it unlocks features that static APIs cannot provide.

Technical Deep Dive

State Management

MCP's implementation of statefulness builds on the foundation of JSON‑RPC. While standard JSON‑RPC treats each request as stateless, MCP embeds unique session tokens and context IDs into every message. This approach allows the server to:

• Establish a session: The server issues a session token and context ID on connection.
• Maintain context: Subsequent requests carry these identifiers, allowing the server to track multi‑step interactions and adjust responses based on accumulated context.

State Management Interaction Diagram

Security

MCP's security is built on a layered approach, incorporating additional steps to safeguard dynamic interactions and state management.

Security Layers Diagram

Error Handling

MCP integrates robust error handling mechanisms to ensure reliable interactions even in complex, stateful sessions.

Error Handling Sequence Diagram

Tools, Prompts, and Resources

MCP differentiates among three types of operations:

• Tools: RPC methods that produce side effects (e.g., creating or updating resources) and require explicit confirmation.
• Prompts: Predefined message templates that guide AI actions without causing side effects.
• Resources: Read‑only endpoints that provide data access.

Comparisons to REST/GraphQL

Conclusion

MCP represents a significant evolution from static, one‑off API integrations to dynamic, stateful connections that adapt in real time. Its three‑element architecture, comprising the client, server, and protocol, enables advanced features like dynamic tool integration and context‑aware interactions that standard JSON‑RPC cannot support due to its stateless nature.

While MCP may not yet be as mature as REST or GraphQL, its enhancements, especially in state management, security, error handling, and bidirectional communication, position it as a promising protocol for the future of agentic AI integration.

Organisations must balance their dynamic capabilities against the technology's increased complexity and evolving maturity.