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Why Build an MCP Server? The Strategic Case
Understand why building MCP servers is critical for the AI transformation. Learn how the shift from app-centric to service-centric architecture will reshape software and why early movers win.
Why Build an MCP Server?
Software architecture is undergoing its biggest transformation since the web. We’re shifting from app-centric to service-centric design, from human teams to human-agent teams, from point-to-point integrations to universal orchestration. MCP servers aren’t just another integration option. They’re how you position yourself for this transformation. The companies building MCP servers today will own the infrastructure layer of tomorrow’s AI economy. The rest will pay to access it.The Architectural Shift
For two decades, we’ve built software as monolithic applications. Each app owns its interface, its logic, its data. Users navigate between apps, copying and pasting, mentally stitching together workflows. This made sense when humans were the only operators. That era is ending. As Microsoft’s Charles Lamanna puts it: “Business apps as we know them are dead.” Not because SaaS companies are going away, but because the interaction model is fundamentally changing. Instead of users navigating to apps, AI agents will orchestrate services. Think about what this means architecturally. Today’s enterprise might have 200 SaaS applications, each with its own UI, its own workflow engine, its own data silo. Tomorrow’s enterprise will have 200 MCP services that AI agents compose into workflows on demand. The UI becomes generative, created in real-time for the specific task. The workflow becomes dynamic, determined by AI based on goals rather than hardcoded paths. Your SaaS application doesn’t become less valuable in this world. It becomes more valuable, but in a different way. Instead of owning the entire user experience, you own a critical capability that thousands of AI agents rely on. Instead of fighting for user attention, you become infrastructure that runs continuously in the background.Teams Are Changing
The most profound change isn’t technological. It’s organizational. Microsoft’s Kevin Scott describes the pattern: every knowledge worker becomes a manager of AI agents. Charles Lamanna goes further: “Teams are groups of people and AI agents.” This isn’t metaphorical. At Microsoft, developers already talk about “the AI agents I work with” the same way they talk about human teammates. An engineer might have a code review agent, a documentation agent, a testing agent, all working asynchronously while they focus on architecture. When teams include agents, everything changes. Meeting schedules don’t constrain productivity because agents work 24/7. Expertise isn’t limited by headcount because each person commands specialist agents. Geographic distribution doesn’t matter because agents are everywhere instantly. But agents need tools. They need access to systems, data, and services. That’s what MCP servers provide. Every MCP server you build is a new capability that millions of AI agents can instantly use. You’re not building for today’s 5 billion internet users. You’re building for tomorrow’s 50 billion AI agents.The Platform Team Opportunity
Platform teams have a unique window of opportunity. Right now, most MCP adoption is bottom-up, driven by individual developers connecting their tools. But enterprises need governance, security, and standardization. The platform teams that move first will define how their organizations build for the next decade. Consider the pattern. Your organization has hundreds of internal services, databases, and tools. Each could be an MCP server. But who decides which get built? How are they secured? How are versions managed? How is access controlled? The platform team that establishes MCP standards early becomes the gateway for all AI automation in the organization. You define the patterns, own the registry, control the policies. This is a rare chance to get ahead of organic growth rather than trying to govern it retroactively. Smart platform teams are already moving. They’re wrapping their three most critical services in MCP, establishing governance patterns, and rolling out to select teams. By the time the organization realizes it needs MCP everywhere, these teams have already defined how it works.Platform teams: See our Platform Overview for detailed implementation patterns and governance models.
Why Not Just Use APIs?
APIs were designed for a world where developers read documentation and write integration code. That world is ending. The cost of point-to-point integration is becoming prohibitive when you need to connect not 10 systems but 10,000. MCP changes the economics of integration. Instead of N×M integrations (every AI tool connecting to every service), you need M servers (one per service). Instead of developers reading docs and writing code, AI agents discover and use services automatically. Instead of brittle hardcoded workflows, you get dynamic orchestration. The technical differences matter too. APIs are stateless; MCP maintains context across interactions. APIs return data; MCP provides tools, resources, and prompts. APIs authenticate each caller separately; MCP centralizes auth at the protocol layer. But the real difference is who the consumer is. APIs are for developers. MCP is for AI agents. And there will be orders of magnitude more AI agents than developers.Three Futures for AI
Where you place your bet on MCP depends on which AI future you believe in. Let’s examine three scenarios:Scenario 1: AI Reaches AGI/SGI
If AI achieves artificial general intelligence or superintelligence, MCP becomes the primary interface between human intent and digital systems. Every service, every database, every tool needs an MCP interface because humans will rarely interact with software directly. The companies that own the MCP layer own the economy’s nervous system. In this scenario, building MCP servers isn’t optional. It’s existential. Services without MCP interfaces become invisible to the AGI layer, effectively ceasing to exist from an economic perspective.Scenario 2: AI Plateaus at Current Levels
Even if AI never improves beyond today’s capabilities, MCP remains valuable. Current AI is already good enough to automate substantial portions of knowledge work. The constraint isn’t AI capability but AI connectivity. In this plateau scenario, MCP servers become the standard integration pattern, replacing REST APIs for any AI-consumable service. The economics alone justify the shift: lower integration costs, higher reuse, better governance.Scenario 3: The Most Likely Path (Continued Scaling)
The most probable scenario is continued improvement at a measured pace. AI gets progressively better at reasoning, planning, and execution. Each improvement makes MCP servers more valuable because agents can handle more complex orchestrations. This is where early movers win decisively. As AI capabilities improve, your MCP servers become more powerful without any changes. The agent that could only query your database last year can now run complex analytical workflows this year. Your investment compounds.The Compounding Advantage
Building MCP servers today creates compounding advantages: Network effects accelerate adoption. As more agents use your MCP server, you gather usage patterns that improve the service. Better service attracts more agents. The cycle reinforces itself. Switching costs create lock-in. Once AI agents depend on your MCP server, replacing it requires retraining agents, updating workflows, and risking production failures. Early adoption creates durable competitive advantage. Data gravity accumulates value. MCP servers see every interaction between AI agents and your service. This data becomes invaluable for optimization, pricing, and product development. Protocol improvements benefit you automatically. As MCP evolves and AI agents improve, your servers become more capable without additional investment.Start Small, Think Big
You don’t need to transform everything at once. Start with one high-value service. Pick something developers already integrate frequently, wrap it in MCP, and watch adoption patterns. Most successful MCP deployments follow this sequence: First, expose read-only operations to limit risk. Then add write operations with audit trails. Next, enable complex workflows that span multiple operations. Finally, add specialized tools that only make sense for AI agents.Ready to build? Start with our Hello World Tutorial to create your first MCP server in 10 minutes.