Model Context Protocol (MCP): Basics and Core Concepts

Understanding how AI models manage context, maintain memory, and interact with applications efficiently.

Introduction

The Model Context Protocol (MCP) defines standards for managing AI model contexts, including prompt history, memory, embeddings, and structured interactions between applications and models. MCP ensures that models can maintain relevant state over multiple queries, provide coherent outputs, and optimize resource usage.

Protocol Specification

MCP specifies the structure and rules for model context management:

• Context Frames: Structured units that store conversation history, relevant facts, or embeddings.
• State Management: Mechanisms for saving, retrieving, and updating model memory efficiently.
• Data Types: Supports text, embeddings, vectors, structured metadata, and optional external references.
• Resource Allocation: Guidelines for token limits, memory quotas, and caching strategies to optimize performance.
• Extensibility: Allows custom memory types, plugins, or external data integrations without breaking compatibility.

Core Concepts

• Context Windows: The portion of model memory used for generating outputs in a single request.
• Embedding Layers: Vector representations of context that models use for semantic understanding.
• Session Management: Maintaining coherent multi-turn interactions across multiple requests.
• Tools & APIs: Interfaces for reading, writing, and modifying model context safely.
• Resource Optimization: Efficient token usage, pruning, and offloading to prevent memory exhaustion.

Example Context Flow

1. Application sends a query to the model with the current context frame.
2. Model retrieves relevant memory and embeddings to generate a response.
3. Output is returned to the application along with an updated context frame.
4. Context frames can be persisted or pruned for future interactions.

Conclusion

The Model Context Protocol provides a standardized approach for maintaining AI model context, enabling coherent, resource-efficient, and extensible interactions. By understanding MCP, developers can design applications that leverage models effectively while managing memory and state optimally.