Demystifying Model Context Protocol (MCP): AI Gets Smarter About Context

The Problem We Had

LLMs are powerful but fragile when context is missing. They forget decisions, repeat themselves, and produce inconsistent outcomes.

The Outcome We Wanted

A standard way for models and tools to persist, share, and prioritize context across sessions.

The Blueprint

Core Principles

• Context persistence: memory survives beyond a single prompt.
• Context sharing: tools and models can publish and consume the same context.
• Prioritization: the right context surfaces at the right time.

Walkthrough Files

• repo/mcp/context-store/schema.json

What Changed

Once MCP is in place, assistants behave less like stateless chatbots and more like reliable operators—because the context doesn’t vanish every time the prompt ends.