Intel MCP is an architectural framework designed to enhance context management and sharing among multi-modal AI agents handling diverse data types like text, images, audio, and video
MCP uses a context engine or orchestrator to manage information flow, ensuring each AI model receives only the most relevant context for its task
Encourages modular AI components, where each model exposes a standard interface and can request or receive modality-specific "context packs
MCP compresses or summarizes data (e.g., object embeddings or document summaries) to reduce memory, processing, and bandwidth usage
Uses shared memory or embedding spaces to align and fuse different data types, enabling cross-modal reasoning and seamless integration
Includes built-in features like access control, anonymization, and auditability to safeguard sensitive user data
MCP supports scalable AI systems by organizing efficient communication between distributed models, reducing computational overhead
Facilitates plug-and-play integration of models and tools from different vendors or architectures, improving system flexibility
MCP is used in conversational AI, robotics, healthcare, and edge AI, enabling intelligent context sharing across devices and systems
A multi-modal recipe creation system uses MCP to analyze food photos, identify ingredients, retrieve recipes, and generate personalized cooking instructions