The Protocol Layer for AI Agents: Inside the Model Context Protocol (MCP) (2/2)
The emerging standard that turns language models into system-level actors.
This part 2 of a 2 part series on the Model Context Protocol (see 1/2 here)
Section 5: The Hard Questions — Risk, Security, Control
The same features that make MCP powerful also makes it open for security risks.
You’re giving LLMs access to real-world tools and data. That opens the door to:
Prompt injection where inputs trick the model into executing unintended actions
Tool misuse especially when semantic descriptions are vague or overlapping
Data leakage through resources passed too liberally or without redaction
Permission drift when agents act beyond their intended scope
Security in MCP is still evolving. There’s no universal sandboxing, no standardized authentication layer, and no formal permission model baked into the protocol.
That doesn’t make it useless. It makes it local-first by necessity.
Right now, MCP is best used in controlled environments:
Local agents with scoped access
Internal workflows where failures are recoverable
Sandboxed toolchains where impact is minimal
As the ecosystem matures, expect to see:
Agent guardrails and policy layers
Semantic firewalls between tools and models
Auditing systems that trace agent behavior and tool usage
MCP doesn’t solve trust. It gives you the machinery to build it—if you do the rest.
That’s why protocol-native design will need to think about governance as it is has done about the architecture.
Section 6: Protocol-Native Futures
The idea of “MCP compatibility” may one day be as common as “REST API support.”
Imagine an ecosystem where tools like Gmail, Notion, GitHub, and Salesforce all expose their capabilities semantically:
So agents can discover, evaluate, and invoke them dynamically
So orchestration happens through reasoning, not hardcoded flows
So systems interconnect based on intent, not brittle glue code
That’s the promise of protocol-native infrastructure.
MCP gives us a glimpse of what that might look like:
Shared grammar for capability discovery
Decoupling of agent logic from environment implementation
Portable, composable AI workflows
We’ll need standards. We’ll need better security models. We’ll need social trust layers around who can expose what.
But if you squint, you can already see it:
Tools building MCP endpoints
Agents evolving toward autonomous orchestration
Developers thinking in terms of protocols
This isn’t just a better integration strategy. It’s a foundational layer for a more semantic, agent-native internet.
Let’s build it.
Closing: Designing the Future with Protocols
By now, it should be clear: MCP isn’t just a cleaner way to wire up AI tools. It’s a blueprint for building systems that are fundamentally more agent-aware, context-driven, and interoperable.
Yes, the risks are real. Yes, the spec is still evolving. But the architectural direction is solid.
If you’re serious about building agents—not just wrappers—then protocol-native design is inevitable. MCP gives you a head start.
Whether you're defining your own MCP server, experimenting with semantic tools, or designing governance layers around AI workflows, you’re not just integrating—you’re laying track for the next infrastructure layer of AI.
Because the future of AI isn’t just more intelligence. It’s better interfaces.
And the interface is the protocol.
Let’s build accordingly.