Without a shared standard, connecting four AI models to five services demands twenty custom integrations — built, maintained, and debugged separately. That's the M×N problem, and it's been quietly strangling agentic workflows since the beginning. Anthropic's Model Context Protocol, MCP, collapses that to M+N. Four models, five services: nine components instead of twenty. Microsoft Research has already documented the performance implications of getting this architecture wrong, and major SaaS vendors are now racing to get it right. While Lecture 5 covered the handoff between design and code, here we focus on MCP's role in tool integration. MCP extends that same logic to the entire tool ecosystem. It's an open-source standard — a universal translator — that lets any compatible AI client communicate with any MCP server, which acts as a secure gateway to external tools and data. Think USB-C for AI connections. Build once, connect everywhere. The architecture has three layers worth understanding. MCP Hosts are the applications you already use — Claude Desktop, Cursor, your IDE. MCP Clients maintain dedicated, secure connections between those hosts and individual servers. And MCP Servers declare their capabilities upfront through a process called Capability Inventory — broadcasting which tools, resources, and prompts they offer. The MCP workflow begins with Discovery: hosts identify available MCP servers, followed by Capability Inventory where servers declare their capabilities. Then the AI requests user permission before touching any external system — database queries, web searches, file manipulation, API calls. That permission layer is the security architecture. AI never gets direct access to critical systems; the MCP server is the gatekeeper. This is what shifts AI from passive text generation to active agent — performing real workflows inside real enterprise systems, not just describing them. Microsoft Research highlights the risks of tool-space interference, where too many active tools can lead to longer task execution and failures. The fix is structural: flattening complex MCP tool parameters improves performance by 47%. MCP also supports hierarchical tool-calling to group large tool catalogs, which reduces interference at scale. Governance matters too. MCP uses a Standards Evolution Process for protocol changes and includes auth and SSO security patterns — because a universal connector is also a universal attack surface if mismanaged. In summary, tools like Cursor, Claude Artifacts, Devin, v0, and Replit Agent operate in isolation without a shared context layer. MCP is that layer. It's the connective tissue that turns a collection of powerful tools into a unified ecosystem where agents share data, maintain context, and hand off work without translation loss. The developers who will architect the most capable pipelines aren't just picking the best individual tools. They're building on standards that make those tools composable. MCP is that standard — and understanding it is what separates a workflow from an architecture.