What Is MCP? How AI Agents Connect to Cloud Infrastructure

Model Context Protocol, or MCP, is becoming the connection layer between AI agents and real systems: APIs, databases, files, developer tools, and cloud infrastructure. For Raff Technologies users, the important question is not only “what is MCP?” but “where should MCP servers and AI automation tools run safely?”

MCP is an open standard that lets AI applications connect to external systems through a consistent interface. Instead of building a custom integration for every AI assistant, tool, database, or API, developers can expose capabilities through MCP servers and let compatible AI clients use them in a more structured way.

The practical infrastructure angle is simple: if an AI agent can read data, call tools, or trigger actions, the server that exposes those capabilities must be hosted, secured, monitored, and isolated properly. That is where cloud VMs, private networking, firewall rules, and careful access control become part of the MCP conversation.

MCP Solves the AI Integration Problem

Before MCP, connecting AI tools to real systems often meant building one-off integrations. One assistant needed one custom connector for one database. Another needed a different connector for the same system. Over time, this creates duplicated work, inconsistent security, and fragile automation.

MCP changes the pattern. A developer can build an MCP server that exposes a system once, then compatible MCP clients can connect to that server through the same protocol.

Think of it this way:

• The AI application is the client.
• The MCP server exposes tools, data, or prompts.
• The external system is the database, API, file store, cloud service, or workflow engine.
• The protocol defines how the client and server communicate.

This matters because AI agents are becoming more useful when they can interact with real systems. A chatbot that only answers from memory is limited. An AI assistant that can query a database, inspect logs, open a ticket, generate a report, or call an internal API becomes much more practical.

MCP Is Not Magic Automation

MCP does not make infrastructure safe by itself. It only standardizes how AI applications connect to tools and data.

That distinction matters.

If you expose a powerful internal API through an MCP server, the protocol does not remove your responsibility to secure it. If the server can restart services, query customer data, edit files, or call billing APIs, then authentication, authorization, logging, and least-privilege access become critical.

The best way to think about MCP is this:

MCP gives AI agents a structured door into your systems. Your job is to decide where that door is hosted, what it can access, and who is allowed to open it.

That is why MCP belongs in the same conversation as cloud security, private networking, and infrastructure operations — not only AI product development.

How MCP Works in Simple Terms

An MCP setup usually has three parts.

First, there is an MCP client. This is the AI application or development tool that wants to use external context or actions. Examples can include AI assistants, coding tools, or agent frameworks.

Second, there is an MCP server. This is the service that exposes specific capabilities. It may provide access to a database, file system, API, search tool, internal workflow, cloud resource, or documentation source.

Third, there is the external system itself. That could be PostgreSQL, GitHub, Slack, a CRM, an internal admin API, a cloud dashboard, or a custom backend service.

The MCP server acts as the controlled bridge. Instead of giving an AI assistant unrestricted access to everything, you expose specific tools and resources intentionally.

A simple MCP workflow might look like this:

• An AI coding assistant asks for context from a repository.
• The MCP client sends a request to the relevant MCP server.
• The server checks what it is allowed to expose.
• The server returns the requested file, metadata, query result, or tool response.
• The AI assistant uses that context to help the user.

That structure is why MCP is useful. It creates a repeatable model for connecting AI to real systems.

Why Developers Should Care

Developers should care about MCP because it reduces integration work and makes AI tooling more practical.

Without a standard protocol, every connection between AI and a tool can become a custom project. With MCP, the goal is to make the connection model more reusable. A team can build or use MCP servers for common systems, then plug them into clients that support the protocol.

This is useful for:

• Codebase-aware AI assistants
• Internal documentation search
• Database query assistants
• Cloud operations tools
• DevOps workflows
• Support automation
• Data analysis workflows
• AI-powered admin dashboards
• Custom internal agents

For developers, MCP is most interesting when the workflow touches real infrastructure. An assistant that can read deployment logs, query a database, inspect service status, or interact with internal APIs is more useful than one that only generates text.

But that usefulness also raises the risk level. The closer an AI system gets to production infrastructure, the more carefully the MCP server must be designed.

Why Cloud Infrastructure Matters for MCP

MCP servers need somewhere to run. For local experiments, a laptop may be enough. For team workflows, internal tools, production-adjacent automation, or long-running agents, local hosting is usually not enough.

A cloud VM gives an MCP server a stable environment with predictable networking, operating system control, and separation from a personal machine. That makes it easier to configure access rules, monitor the process, update dependencies, and keep the service available for the team.

A Raff Linux VM is a practical starting point for MCP server experiments because you can create a clean server environment, install only what the project needs, and isolate the workload from your main application infrastructure.

This is especially useful when your MCP server needs to communicate with other cloud services, private APIs, databases, automation tools, or internal dashboards.

Practical MCP Use Cases for Cloud Teams

MCP becomes more interesting when it connects AI agents to infrastructure and developer workflows.

Here are practical examples.

The important word is “controlled.” MCP should expose limited capabilities with clear boundaries. For example, a read-only database MCP server is safer than one that can modify production records. A server that summarizes logs is safer than one that can restart services.

The Security Problem MCP Introduces

MCP security matters because MCP servers can sit between AI agents and sensitive systems.

A poorly designed MCP server can expose too much data, execute unsafe commands, leak credentials, or give an AI agent access it should not have. This risk becomes higher when the server connects to infrastructure tools, databases, file systems, or admin APIs.

Before deploying an MCP server, ask these questions:

• What data can this server access?
• Can it only read, or can it also write?
• Which users or clients can connect?
• Are credentials stored safely?
• Are requests logged?
• Can actions be audited later?
• Is the server reachable from the public internet?
• Does the server need private networking?
• What happens if the AI client makes a bad request?
• Can the server be rolled back or disabled quickly?

A safe MCP design starts with least privilege. Expose the minimum set of tools needed for the workflow, then expand only when there is a clear reason.

Hosting MCP Servers on Raff

A Raff VM can host MCP servers for development, testing, internal tools, or production-adjacent automation. The right architecture depends on what the server can access.

For a low-risk experiment, you might run one MCP server on a small VM with limited test data. For an internal team tool, you may want a dedicated VM, firewall restrictions, private networking, and stronger monitoring. For a sensitive production workflow, you should isolate the MCP server from public access and expose only narrow, auditable capabilities.

A practical Raff setup might include:

• A dedicated Raff VM for the MCP server
• Firewall rules that limit inbound access
• SSH key-based administration
• Private networking for internal service communication
• Separate credentials for each connected system
• Read-only permissions where possible
• Backups for configuration and workflow data
• Logs for audit and troubleshooting

If the MCP server connects to private services, a public server with broad access is the wrong model. Use tighter network boundaries and expose only what the workflow actually needs.

MCP and Private Networking

Private networking becomes important when MCP connects AI tools to systems that should not be publicly reachable.

For example, an MCP server may need to query an internal API, inspect a database, or talk to a worker service. Those systems should not be opened to the internet just because an AI assistant needs context.

A better model is to place the MCP server near the systems it needs to access, then restrict external access to the MCP layer itself. That lets you build a controlled bridge rather than exposing every backend service directly.

Raff’s Private Cloud Networks product path is relevant here because MCP is fundamentally about controlled connectivity. The safer pattern is not “AI can reach everything.” The safer pattern is “AI can reach one carefully designed service that has limited access to the right systems.”

MCP and Automation Workflows

MCP also fits naturally with automation tools. An AI agent may not need direct access to every system. Sometimes it should trigger a controlled workflow instead.

For example, instead of giving an AI agent direct permission to modify infrastructure, you could expose a tool that triggers a predefined workflow:

• Create a staging environment
• Run a backup check
• Generate a deployment report
• Open a support ticket
• Summarize failed jobs
• Run a safe diagnostic script

This is a healthier automation model. The AI agent requests a known operation, and the workflow system performs the steps with guardrails.

If your team already uses workflow automation, Raff’s article on n8n self-hosted automation is a useful companion topic. n8n handles workflow orchestration; MCP can help AI agents interact with tools and workflows through a standardized interface.

What I Would Test Before Deploying MCP Seriously

Before using MCP for anything important, I would test the server in a clean environment.

First, test the permissions. Start with read-only access and prove the workflow works before adding write actions. If the server only needs to read documentation, it should not have access to production databases.

Second, test failure behavior. What happens if the external API times out, returns bad data, or rejects authentication? The MCP server should fail safely, not expose internal errors or retry dangerous actions.

Third, test logging. You should know which client requested which tool, when it happened, and what result came back. If an AI agent performs an action through MCP, you need an audit trail.

Fourth, test network exposure. If the MCP server does not need to be public, do not make it public. If it must be reachable, restrict who can reach it.

Fifth, test rebuildability. A clean VM should be easy to recreate with documented setup steps. If the server becomes important, the deployment process should not depend on one person’s memory.

This is why I prefer testing infrastructure tools on fresh VMs. A clean VM shows whether the setup is actually repeatable.

When MCP Is a Good Fit

MCP is a good fit when your team wants AI tools to interact with real systems in a structured way.

Use MCP when:

• You need AI agents to access internal tools
• You want reusable integrations instead of one-off connectors
• You need controlled access to APIs, files, databases, or workflows
• You want AI assistants to understand your operational context
• You are building developer tools or internal automation
• You want to standardize how agents connect to systems

Avoid MCP when the workflow is simple enough to solve with a normal API call, scheduled script, or basic automation rule. MCP is powerful, but it still adds another layer to operate.

What This Means for Raff Users

For Raff users, MCP should be viewed as part of the infrastructure stack when it touches cloud systems.

A developer can run an MCP server on a VM. A team can place that server near internal tools. An operator can restrict access with firewall rules and private networking. A founder can use MCP to explore AI automation without giving uncontrolled access to production systems.

The safest starting point is small:

• Pick one useful workflow.
• Use a dedicated VM.
• Start with read-only access.
• Limit network exposure.
• Log every request.
• Test failure paths.
• Add write actions only when necessary.

You can start with a Linux VM, review Raff pricing, and connect the MCP idea to broader cloud automation using Raff API keys for small-team automation.

Final Thoughts

MCP is useful because it gives AI agents a standard way to connect with the tools developers already use. But the real value appears when the protocol is paired with secure infrastructure.

The question is not only whether an AI assistant can connect to a tool. The question is whether that connection is limited, observable, recoverable, and hosted in the right place.

That is why the cloud layer matters. If MCP servers become part of your automation stack, they deserve the same care as any other infrastructure component: isolation, access control, backups, monitoring, and clear ownership.

Start with one MCP server. Keep permissions narrow. Run it in a clean environment. Then expand only when the workflow proves its value.