When Anthropic’s Model Context Protocol (MCP) first appeared, it might have seemed like a developer-focused tweak — a technical abstraction to tidy up integrations. But MCP is rapidly becoming something much larger: a core standard for managing persistent memory in AI systems. As language models take on more critical responsibilities, the need for tools that make them context-aware, interoperable, and secure has never been greater.

Let’s dig into what MCP really is, what makes it powerful, and what organizations must understand before embracing it.

A Shift in How AI Thinks and Remembers

Large language models are, by nature, stateless — each prompt is a blank slate. But real-world tasks demand continuity, memory, and awareness of prior steps. You need your AI to remember what happened in a previous interaction, or what was said five minutes ago. This is where MCP comes in.

According to the protocol’s formal definition, MCP offers a standard interface for AI systems to manage context, interact with tools, and exchange data in real time. It defines how context is created, stored, updated, and deleted — enabling dynamic memory across user sessions and systems.

The MCP Architecture: Under the Hood

Per the published architecture, the protocol follows a three-part model:

• MCP Host: The environment where the AI runs — such as a coding assistant or AI dashboard.

• MCP Client: A local agent that sits inside the host and communicates with external systems.

• MCP Server: The gateway to the outside world, exposing three core capabilities:

  • Tools – APIs and functions the AI can call

  • Resources – Documents, databases, or files the AI can access

  • Prompts – Predefined templates that standardize how the AI interacts with systems

This architecture enables an AI assistant to scan logs, invoke APIs, or update knowledge — all without bespoke integration code. MCP essentially provides a plug-and-play memory and command interface for AI.

MCP Is a Game-Changer

🧠 Context That Persists

MCP gives AI models long-term memory by letting them persist and reference stored context across queries. This leads to more coherent, relevant, and accurate outputs.

⚙️ One Protocol, Many Systems

It eliminates the need for handcrafted integrations by offering a standardized structure that works across apps, tools, and teams — much like the Language Server Protocol did for dev environments.

📈 Built to Scale

Because of its modular nature, MCP lets organizations rapidly scale AI usage — securely — by separating model intelligence from integration logic.

Built-In Lifecycle: Context Flows

The MCP paper outlines a lifecycle for handling context and services that includes creation (registering context, tools, or resources when a session begins), operation (continuously retrieving or updating information based on task needs), and update or removal (changing context or removing it to optimize relevance and memory usage).

This lifecycle ensures AI systems have access to just the right information at the right time, while maintaining efficiency and control.

Real-World Adoption and Use Cases

There is no shortage of real-world applications. The MCP paper highlights adoption across multiple companies and products. Claude uses MCP to invoke tools in its desktop and IDE-based environments. OpenAI, Gemini, Llama support MCP, establishing it firmly as the standard. Cursor, Replit, Codeium, and Zed use MCP to power coding copilots that query logs or run local scripts. Apollo and Block adopted MCP to integrate AI with their internal workflows.

These integrations showcase MCP’s ability to unify context management and tool execution — empowering models to operate across a wider surface area without custom work per use case.

Let’s Be Real — It’s Not All Easy

While the benefits of MCP are far-reaching, setup and permissioning is still complex. Implementing MCP requires careful definition of what context to keep, how to store it, and how to revoke it. There’s engineering complexity in standing up the protocol securely. It’s far from plug-and-play. On top of that, permissions can easily go sideways. With MCP, AI systems get access to multiple systems. If misconfigured, this could lead to overexposure of sensitive data or services.

Security Risks: New Powers, New Threats

The paper calls out key attack surfaces unique to MCP’s persistent, connected nature:

• Context Leakage – Stored history could include passwords, secrets, or PII if not scrubbed.

• Prompt Injection – Attackers could manipulate stored prompts to bypass safety mechanisms.

• Broad Access Scope – MCP servers often interface with many systems, increasing the blast radius of compromise.

• Tool Poisoning – A malicious tool exposed via MCP could trick the AI into unsafe behavior.

• Session Hijacking – Without tight session controls, attackers might impersonate users or replay sensitive requests.

These are not speculative risks — they’re credible vectors that blend traditional security concerns with AI-native threats.

Stay Secure with MCP

To mitigate these, the MCP paper and real-world usage suggest strict session management with short-lived, validated tokens and enforced timeouts. Organizations must adopt least privilege by default and expose only what’s needed to the model, nothing more. Along with this, it’s imperative to integrate this with a Zero Trust strategy, and treat the AI agent and MCP servers as potentially untrusted. And finally, it’s important to audit everything – log and monitor tool calls, context changes, and access requests.

MCP Integration Playbook

If you’re exploring MCP adoption, start with these easy steps:

1. Map Your Existing Stack – Identify where LLMs talk to tools and where MCP can simplify or replace integrations.

2. Establish Guardrails – Establish what kinds of context are allowed, retention policies, and tool access.

3. Take Baby Steps – Roll out MCP in a sandboxed use case (e.g. querying internal logs) before scaling to critical systems.

4. Train Your Teams – Devs and security engineers should understand both the power and the pitfalls of MCP.

The Bottom Line

MCP is not just another integration layer — it’s a foundational protocol for making AI more contextual, capable, and collaborative. It brings structure to AI memory, modularity to AI access, and scale to AI interaction.

But just as important as what MCP enables is what it demands: security-first thinking, smart design, and ongoing vigilance.

As the MCP paper makes clear: context is power — but it’s also a liability if unmanaged.

At Natoma, we're building a new layer in the AI stack. MCP is shaping how that layer remembers, interacts, and scales — responsibly.