Everyone’s talking about the Model Context Protocol (MCP) — but is it really the SaaS killer or just another developer tool? Here’s a practical take on what it gets right, what it misses, and what’s coming next.
The MCP Hype Cycle
MCP burst onto the scene with bold claims: a standardized protocol for AI agents to interact with any tool or data source. The vision is compelling—agents that can orchestrate complex workflows across your entire tech stack through a unified interface.
But between vision and reality lies implementation. Let’s examine what MCP actually delivers today.
What MCP Gets Right
Standardization Matters
Before MCP, every AI integration required custom code. Connect to Slack? Write Slack-specific logic. Connect to GitHub? Different logic. MCP provides a standard interface that reduces this fragmentation.
Context as a First-Class Concept
MCP treats context—the information an agent needs to do its job—as a core primitive. Resources, tools, and prompts all flow through the same protocol. This consistency makes systems easier to reason about.
Local-First Design
MCP servers run locally, meaning sensitive data doesn’t automatically route through third-party services. For enterprises with strict data governance, this matters.
What MCP Misses (Today)
Authentication Complexity
Real enterprise systems have complex authentication: OAuth flows, service accounts, role-based access, token refresh. MCP’s current auth model is simplistic compared to production requirements.
State Management
Long-running workflows need state. MCP’s stateless request-response model works for simple queries but struggles with multi-step processes that span minutes or hours.
Error Handling
Production systems fail in complex ways. Retries, partial failures, compensation logic—MCP’s error model doesn’t address these operational realities.
Observability
When an MCP-orchestrated workflow fails, debugging requires visibility into every step. Current tooling provides limited observability compared to mature integration platforms.
Where MCP Shines
MCP excels in specific scenarios:
- Developer tooling - IDEs and coding assistants benefit from standardized access to code repositories, documentation, and development tools
- Internal automation - Controlled environments with trusted users and limited scope
- Prototyping - Quick experiments that don’t need production-grade reliability
Where to Be Cautious
Proceed carefully when:
- Customer-facing systems - Reliability requirements exceed MCP’s current maturity
- Regulated industries - Compliance needs outstrip available audit and control capabilities
- Complex workflows - Multi-system orchestration with error handling requirements
The Practical Path Forward
For teams evaluating MCP:
Start Small
Pick a contained use case with limited blast radius. Internal developer tools are a good starting point.
Build Guardrails
Wrap MCP interactions with logging, rate limiting, and fallback mechanisms. Don’t assume the protocol handles failure gracefully.
Plan for Evolution
MCP is evolving rapidly. Design systems that can adapt as the protocol matures rather than betting everything on current capabilities.
Evaluate Alternatives
MCP isn’t the only option. OpenAPI, GraphQL, and traditional API approaches have decades of production hardening. Sometimes boring technology is the right choice.
The Realistic Timeline
MCP will improve. The community is active, major players are invested, and real feedback drives development. But production readiness for enterprise workloads is likely 12-24 months away, not 3-6 months.
The Bottom Line
MCP represents a real step forward in AI-tool integration. The vision is sound, and the protocol addresses genuine pain points. But hype often outpaces reality in emerging technology.
Evaluate MCP based on your specific requirements, not the promise of what it might become. Use it where it fits today, and stay informed as it evolves.