tags:: #LLMs #PROJECTS/MOSAICO #Reading

- MCP has quickly become a cornerstone in enabling AI models—particularly large language models (LLMs) and autonomous agents—to interact with external tools, APIs, and data sources through a standardized interface. Much like USB-C revolutionized device connectivity, MCP aims to standardize how models access and apply contextual information.
- While MCP initially focused on simplifying integrations, its broader impact is now coming into view. We're beginning to see MCP evolve into a foundational layer for distributed AI systems. These systems involve not just a single model with a static toolbox, but networks of interoperating agents, dynamically discovering, invoking, and coordinating with external resources.
- This article explores where MCP is headed in the next 3–5 years. We look at five key areas: **federated discovery, decentralized MCP networks, persistent and transferable context, trust and cryptographic guarantees, and AI-native protocol interactions such as context negotiation and orchestration**. These directions mark a shift from tool access to context-rich, agentic ecosystems.