Preample

This block is me. The stuff below was prepared by my colleague Claude Sonnet (3.7).

The lines on the protocols go like this :

The Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools.

Agent2Agent : An open protocol enabling communication and interoperability between opaque agentic applications.

I think it's fair to say that seamlessness and opacity are relative terms in this context. Also that these specs are a step in the right direction. However, they're not with flaws.

Recently I've been juggling a lot of pies.

The Ontologies

We're excited to announce two major developments in the world of agent interoperability and semantic context exchange: the release of the Agent-to-Agent (A2A) Ontology and a significant revision to the Model Context Protocol (MCP) Ontology. These complementary ontologies provide a formal semantic foundation for agent communication and context management, enabling more sophisticated AI systems that can effectively share information and collaborate.

The A2A Ontology focuses on structured communication between autonomous agents, defining a framework for tasks, messages, artifacts, and skills. Meanwhile, the revised MCP Ontology enhances the representation of resources, tools, and prompts that language models can leverage to access external context. Together, they create a comprehensive semantic ecosystem that bridges the gap between agent intelligence and the rich world of linked data.

Both ontologies use standard RDF/OWL representations, making them immediately compatible with existing semantic web technologies and knowledge graphs. They're designed to address real-world challenges in building interconnected, context-aware AI systems that can leverage distributed information and capabilities.

If you're working with language models, you've likely encountered two fundamental challenges: providing relevant context to your models and enabling them to collaborate with other AI systems. The MCP and A2A ontologies directly address these pain points.

The MCP Ontology creates a standardized way to represent external resources, tools, and prompts. This means your language models can access and understand a wide range of contextual information through a consistent interface. Instead of building custom integrations for every data source or tool, you can leverage the semantic structure of MCP to create a unified context layer. This is particularly valuable for RAG implementations, where the ontology provides a rich framework for resource discovery, relationship traversal, and metadata filtering that goes beyond simple vector similarity.

The A2A Ontology complements MCP by enabling structured communication between agents. It provides a formal representation of tasks, messages, and artifacts that allows different agent systems to collaborate effectively. This means your language model-based agents can delegate subtasks, request information, or provide services to other agents in a standardized way. The ontology includes concepts for tracking task states, defining agent capabilities through skills, and exchanging multimodal content.

These ontologies don't require you to rewrite your existing systems. They provide a semantic layer that enhances interoperability while allowing you to continue using your preferred LLM frameworks. By adopting these standards, your AI applications gain the ability to participate in broader agent ecosystems and leverage distributed capabilities.

From a semantic web perspective, these ontologies represent a significant step toward integrating modern AI capabilities with established knowledge representation standards.

The A2A Ontology defines core classes including a2a:AgentCard, a2a:Task, a2a:Message, a2a:Artifact, and a2a:Skill, along with properties that establish their relationships. It includes SHACL shapes for validation and follows best practices for ontology design. The design emphasizes task state management, capability representation, and multimodal content exchange, all while maintaining compatibility with existing agent frameworks.

The MCP Ontology focuses on the connection between language models and external context. It defines classes like mcp:Server, mcp:Resource, mcp:Tool, and mcp:Prompt, providing a structured way to represent the resources and capabilities available to language models. The revision enhances the ontology with improved resource typing, tool parameter representation, and content modeling.

Both ontologies are designed to integrate seamlessly with existing knowledge graphs and ontologies. They can be extended with domain-specific concepts while maintaining interoperability through their core vocabularies. The ontologies support standard SPARQL queries for discovery and can be used with existing RDF stores and reasoners.

From an implementation perspective, the ontologies come with comprehensive documentation, example SPARQL queries, and SHACL validation rules. They're designed to be practical tools for knowledge engineers working on AI systems, bridging the gap between traditional semantic web applications and the emerging world of language model-based agents.

We invite the semantic web community to explore these ontologies, provide feedback, and contribute to their evolution as we work to create a more interconnected, intelligent web of knowledge and capabilities.

Ready to get started with A2A and MCP? Check out the GitHub repositories for documentation, examples, and implementation guides. We welcome your contributions and feedback as we continue to develop these standards for agent interoperability and context exchange.