#84 — Why MCP is useful: An introduction to MCP for skeptics

The Model Context Protocol (MCP) has been making waves in developer circles, triggering passionate debates about whether it's revolutionary infrastructure or just another overhyped solution looking for a problem. For startup founders watching from the sidelines, here's your complete playbook for understanding, evaluating, and implementing MCP to build sustainable competitive advantages.§

The bottom line up front

MCP transforms how AI assistants connect to your existing tools and data sources. Instead of building custom integrations for every service your team uses, MCP creates a standardized way for AI to talk to everything from your CRM to your codebase. Think of it as the universal adapter for AI-powered workflows.§

The key insight: MCP isn't about the AI itself—it's about making your existing business tools AI-accessible without rebuilding everything from scratch. This is the difference between having an AI assistant that can only chat versus one that can actually execute complex workflows across your entire tech stack.§

Why founders should care (beyond the hype)

Faster time-to-AI-value: Your team already uses dozens of tools. MCP lets you connect AI assistants to these existing workflows without custom development work. Need your AI to pull customer data from Salesforce, check inventory in your warehouse system, and update project status in Notion? MCP makes this possible through a single protocol.§

Reduced integration debt: Every custom AI integration your team builds becomes technical debt. MCP standardizes these connections, meaning you write integration code once and it works across different AI platforms. This is architectural thinking, not just feature development.§

Competitive moat through AI workflows: While your competitors are still manually switching between tools, your team could be using AI that seamlessly orchestrates complex multi-step processes across your entire tech stack. This isn't just efficiency—it's a fundamental operational advantage.§

Plugin architecture benefits: MCP operates like a plugin system where the client speaks one protocol to any number of specialized servers. Adding Slack integration means deploying a Slack MCP server—no client changes required. This separation of concerns prevents integration bloat and maintains system flexibility.§

Strategic Assessment Framework

The reality check: When MCP makes sense

You're a good fit if:§

• Your team uses 5+ SaaS tools daily
• You're already experimenting with AI assistants like Claude or ChatGPT
• You have basic technical resources (or can hire them)
• Your workflows involve repetitive tasks across multiple systems
• You're building for the next 3-5 years, not just solving immediate problems

Skip it if:§

• You're pre-product-market fit and need to focus on core features
• Your team is non-technical and you're bootstrapping
• You're already satisfied with simple AI chatbot interactions
• You can't dedicate engineering time to learning new protocols

The nuanced middle ground: If you're between these extremes, start with one specific workflow that spans 2-3 tools. Test the value proposition before committing to broader implementation.§

What the critics get wrong (and right)

The "it's over-engineered" crowd has a point about complexity, but they're missing the forest for the trees. Yes, MCP uses JSON-RPC over stdio and has its own transport negotiation. But for founders, the question isn't whether it's elegant—it's whether it solves real problems faster than alternatives.§

The security concerns are legitimate but manageable. MCP servers run code on your systems, so treat them like any other software dependency: review before installing, use trusted sources, and implement proper access controls. The security model is the same as any code execution environment—don't run code you don't trust.§

The "just use REST" argument misses the point entirely. MCP enables multistep workflows with persistent context, dynamic tool discovery, bidirectional communication, and session management across tools. These aren't CRUD operations—they're conversational interactions that require state management.§

The competitive landscape reality

Major players are already building MCP ecosystems. Anthropic (Claude), along with developers from Cloudflare, LangChain, and Vercel, are actively contributing to the specification. The community has built over 15,000 MCP servers in just six months.§

Translation for founders: This isn't a science experiment. It's becoming infrastructure that your AI-forward competitors will likely adopt. The question is whether you'll be early enough to gain advantages or late enough to be playing catch-up.§

Market timing indicators: Companies are already building MCP-based developer tools, MCP app stores, AI automation services, and consulting practices around implementation. The ecosystem is moving beyond experimental to commercial.§

Implementation and Execution

Implementation strategy for startups

Phase 1: Proof of concept (Week 1-2)§

• Pick one repetitive workflow that spans 2-3 tools
• Build or find an MCP server that connects them
• Measure the time savings and workflow improvement
• Document what works and what doesn't

Phase 2: Strategic expansion (Month 1-3)§

• Identify 3-5 high-impact workflows for MCP integration
• Think ecosystem: choose implementations that scale as you add tools
• Budget for learning curve: your technical team needs time to understand MCP concepts
• Factor protocol learning into sprint planning

Phase 3: Competitive advantage (Month 3-6)§

• Build proprietary MCP servers for your unique business logic
• Create AI workflows that competitors can't easily replicate
• Use MCP to enable customer-facing AI features
• Consider building MCP servers as products themselves

Technical implementation notes:§

• MCP servers can be simple scripts or complex cloud services
• The protocol supports stdio, Streamable HTTP, and other transports
• Dynamic tool discovery allows servers to add/remove capabilities at runtime
• Resources, prompts, and completables extend basic tool functionality

Business model opportunities

For technical founders:§

• Build MCP-based developer tools that simplify adoption
• Create MCP monitoring and performance tracking tools
• Develop specialized MCP servers for vertical markets
• Offer MCP integration platforms for enterprises

For non-technical founders:§

• MCP consulting and implementation services
• No-code/low-code AI automation platforms using MCP
• Industry-specific AI workflow solutions
• MCP marketplace and discovery platforms

Revenue model considerations: The MCP ecosystem needs sustainable monetization. Think mobile app marketplace models—premium connectors, automation scripts, and enterprise integrations can create new revenue streams while fostering innovation.§

Enterprise considerations

Security and trust challenges: Current MCP relies on honor-based systems. Enterprises need centralized authorities or "Play Store"-like models for MCP connectors with vulnerability scans and robust trust chains.§

Cloud/SaaS limitations: Claude only supports MCP in desktop apps, not web platforms. Self-hosting requirements create deployment friction. Fully managed MCP SaaS offerings could eliminate these barriers.§

Workflow orchestration gaps: Current MCP clients operate as atomic entities. Enterprises need workflow automation layers that enable complex processes across multiple connectors.§

Deployment complexity: Setting up MCP clients with correct JSON configuration shouldn't require hours of troubleshooting. Streamlined deployment resembling mobile app installations is necessary for adoption.§

Risk mitigation strategies

Protocol evolution risk: MCP is still maturing. Build abstractions that can adapt to specification changes. Don't couple your core business logic too tightly to current MCP implementations.§

Vendor lock-in concerns: While MCP is open source, ensure your implementations can work with multiple AI platforms. Don't build exclusively for Claude or any single provider.§

Security and compliance: Implement proper access controls, audit trails, and data governance from day one. Treat MCP servers as critical infrastructure components.§

Talent and expertise: MCP requires technical sophistication. Plan for hiring or training developers who can work with the protocol effectively.§

Success metrics and KPIs

Immediate metrics (0-3 months):§

• Time saved on specific workflows
• Number of manual steps eliminated
• Error reduction in multi-tool processes
• Developer productivity improvements

Strategic metrics (3-12 months):§

• Customer feature adoption enabled by MCP
• Revenue impact from AI-powered capabilities
• Competitive advantages gained through workflow automation
• Technical debt reduction from standardized integrations

Long-term indicators (12+ months):§

• Market position relative to AI-native competitors
• Customer retention improvements from AI features
• New revenue streams enabled by MCP capabilities
• Operational leverage from AI workflow automation

Strategic Decision Framework

The founder's strategic framework

Evaluate MCP through these lenses:§

Operational efficiency: Will MCP-enabled workflows save significant time on repetitive tasks? Can you quantify the productivity gains?§

Competitive differentiation: Can you build AI workflows that competitors can't easily replicate? Do you have proprietary data or processes that become more valuable through AI integration?§

Customer value creation: Can MCP enable customer-facing features that increase retention or willingness to pay? Does it unlock new product capabilities?§

Technical debt management: Will MCP reduce long-term integration complexity, or add another layer to maintain?§

Market positioning: Are you building for an AI-native future, or trying to retrofit AI onto existing processes?§

The founder's verdict

MCP represents a bet on AI-native workflows becoming standard business practice. For founders building for the next 3-5 years, ignoring this trend could mean watching competitors automate processes you're still doing manually.§

The protocol has rough edges and the tooling is still maturing. But the core value proposition—standardized AI-tool integration—addresses a real problem that will only grow as AI adoption accelerates.§

The strategic question: Will your startup be building AI workflows or still debating whether they're worth the complexity?§

The timing consideration: Early adopters gain advantages from learning curves and ecosystem positioning. Late adopters face commoditized solutions and established competitive moats.§

The execution reality: MCP success requires technical commitment, strategic thinking, and patience with emerging technology. It's not a quick fix—it's infrastructure for AI-native operations.§

This isn't just about adopting a new protocol. It's about positioning your startup for a future where AI workflows are table stakes, and competitive advantage comes from how well you've integrated intelligence into your operations.§

MCP servers are available for most major business tools, with new integrations launching weekly. The specification is open source and backed by major AI companies. The question isn't whether MCP will matter—it's whether you'll be ready when it does.§