MCP Servers Explained: Claude’s New AI Backbone for Real Automation

October 4, 2025

#MCP server #Claude #OpenShift #ToolHive #AI automation #DevOps #Anthropic #cloud computing

MCP Servers Explained: Claude’s New AI Backbone for Real Automation

Every once in a while, a new protocol quietly lands in the tech world and changes everything. Model Context Protocol (MCP) is one of those. It’s not flashy on the surface, but it’s the hidden infrastructure that’s turning models like Claude into something far more powerful than a chatbot. With MCP servers, an AI model can directly connect to your apps, databases, cloud tools, and business systems—securely, contextually, and automatically.

If that sounds a bit abstract, think of it this way: before MCP, AI models could think and generate text, but they were trapped in isolation. Now, with MCP servers acting like a universal plug system, they can do. They can fetch your calendar events, summarize unread emails, generate reports, push data to Notion, or even deploy code through GitHub—all within one continuous conversation.

In this long-form deep dive, we’ll unpack what MCP servers actually are, how they differ from traditional APIs, and how you can deploy and scale them in production—specifically on OpenShift using ToolHive, a developer environment purpose-built for managing AI infrastructure.

What Is an MCP Server?

The Model Context Protocol in a Nutshell

MCP stands for Model Context Protocol. It’s a communication standard that lets large language models (LLMs) like Claude or ChatGPT securely interact with external systems. You can think of it as a USB port for AI. Plug in the right adapter (an MCP server), and suddenly your model can talk to any supported system.

Each MCP server acts as an adapter layer between the model and a specific service. For example:

A GitHub MCP server manages commits, pull requests, and issues.
A Slack MCP server sends messages or retrieves chat threads.
A PostgreSQL MCP server executes SQL queries or retrieves analytics data.
A Canva or Figma MCP server can generate design assets or templates.

From the model’s perspective, each MCP server exposes a set of tools—functions the model can call as part of its reasoning process. When you ask Claude to “summarize this week’s meetings and update my Notion dashboard,” it doesn’t just generate text. It:

Calls the Calendar MCP to fetch events.
Calls the Notion MCP to find the correct dashboard.
Writes updates using structured tool calls.

All this happens in real time, with minimal setup on your end. MCP is what makes that orchestration possible.

Why MCP Matters

Before MCP, integrating an AI model with your workflow required cumbersome custom APIs, brittle scripts, or third-party middleware. MCP standardizes that process, making AI connectivity modular and extensible. This has several big advantages:

Security-first design: MCP servers authenticate independently, so you can grant granular permissions to each tool.
Composable automation: You can combine multiple MCP servers to chain complex workflows.
Cross-platform flexibility: The same MCP can be used by different AI models adhering to the protocol.
One-click installation: Thanks to DXT (Desktop Extension) files, many servers install like Chrome extensions.

The result? AI that acts less like a conversation partner and more like a personal assistant—or even a digital employee.

How MCP Servers Work

Let’s unpack the architecture a bit. Each MCP server consists of three main layers:

Connector Layer – Handles communication between the LLM and the external API.
Schema Definition Layer – Defines the tool schema (available functions, parameters, and data types) that the model can access.
Runtime Execution Layer – Executes the requested actions, handles authentication, and returns structured results.

Here’s a simplified JSON representation of an MCP server’s schema:

{
  "name": "github-mcp-server",
  "description": "MCP server for interacting with GitHub repositories.",
  "tools": [
    {
      "name": "create_issue",
      "parameters": {
        "repo": "string",
        "title": "string",
        "body": "string"
      },
      "output": {
        "issue_url": "string"
      }
    },
    {
      "name": "get_pull_requests",
      "parameters": { "repo": "string", "state": "string" },
      "output": { "pull_requests": "array" }
    }
  ]
}

When Claude sees this schema, it knows it can call create_issue or get_pull_requests just as a developer would call a function. The model uses reasoning to decide when to invoke a tool and passes the right parameters.

Deploying MCP Servers on OpenShift

Now for the practical part. Deploying an MCP server isn’t particularly hard, but doing it right—at scale, securely, and with good observability—requires care. OpenShift is a great platform for this because it combines Kubernetes orchestration with enterprise-grade tooling for AI workloads.

Why OpenShift?

Red Hat OpenShift provides:

Container-native orchestration for running MCP servers as microservices.
Built-in CI/CD pipelines for automated deployment.
Service Mesh integration for securing inter-service communication.
Secrets management via Kubernetes secrets or HashiCorp Vault.
Developer tools like ToolHive for managing AI service deployments.

These make it ideal for hosting multiple MCP servers across environments (dev, staging, prod) while keeping everything auditable and scalable.

Step 1: Containerizing the MCP Server

Start by containerizing your MCP server. Most MCP servers are Node.js or Python-based. Here’s an example Dockerfile for a simple Node.js-based GitHub MCP server:

# Dockerfile
FROM node:20-alpine
WORKDIR /app
COPY package*.json ./
RUN npm install --production
COPY . .
EXPOSE 8080
CMD ["node", "server.js"]

Build and push the image:

docker build -t quay.io/yourorg/github-mcp:latest .
docker push quay.io/yourorg/github-mcp:latest

Step 2: Creating an OpenShift Deployment

Once the image is ready, define a deployment YAML file:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: github-mcp-deployment
spec:
  replicas: 2
  selector:
    matchLabels:
      app: github-mcp
  template:
    metadata:
      labels:
        app: github-mcp
    spec:
      containers:
      - name: github-mcp
        image: quay.io/yourorg/github-mcp:latest
        ports:
        - containerPort: 8080
        env:
        - name: GITHUB_TOKEN
          valueFrom:
            secretKeyRef:
              name: github-credentials
              key: token

Apply it:

oc apply -f github-mcp-deployment.yaml

This spins up your MCP server as a containerized service inside OpenShift.

Step 3: Exposing and Securing the Service

To allow Claude (or another model) to access your MCP server, you’ll need to expose it securely.

apiVersion: v1
kind: Service
metadata:
  name: github-mcp-service
spec:
  selector:
    app: github-mcp
  ports:
  - protocol: TCP
    port: 80
    targetPort: 8080

Then create a route with TLS termination:

oc create route edge github-mcp --service=github-mcp-service --hostname=github-mcp.apps.yourcluster.example.com

Your MCP endpoint will now be available at the specified hostname with HTTPS enabled.

Step 4: Registering the MCP Server with ToolHive

ToolHive is Red Hat’s developer platform for managing AI tools and MCPs. You can register your MCP server using the CLI:

toolhive register mcp github-mcp \
  --url https://github-mcp.apps.yourcluster.example.com \
  --auth-type token \
  --token $GITHUB_TOKEN

Once registered, ToolHive can monitor health, manage updates, and handle version rollbacks automatically.

Managing Multiples MCP Servers in a Cluster

In most real-world scenarios, you’ll deploy multiple MCP servers—one for each external system. OpenShift makes it easy to manage them under a single namespace and apply shared policies.

Example: Multi-MCP Deployment Architecture

+-----------------------------+
|        OpenShift Cluster    |
|-----------------------------|
| Claude AI Model             |
|   ↳ ToolHive Controller     |
|   ↳ GitHub MCP Server       |
|   ↳ Notion MCP Server       |
|   ↳ Slack MCP Server        |
|   ↳ PostgreSQL MCP Server   |
+-----------------------------+

Each MCP server runs as its own pod with autoscaling enabled. The ToolHive Controller communicates with them over internal services, ensuring secure interconnection and centralized logging.

Autoscaling & Observability

You can set up autoscaling rules based on CPU or memory usage:

oc autoscale deployment github-mcp-deployment --min=2 --max=10 --cpu-percent=70

For observability, integrate Prometheus and Grafana dashboards via OpenShift’s built-in monitoring stack. ToolHive automatically exports MCP metrics like request latency, tool invocation counts, and error rates.

Real-World Use Cases

1. Content Pipeline Automation

Let’s say you run a content-heavy operation—marketing, editorial, or media. You can connect Claude to:

Canva MCP: Generate graphics.
WordPress MCP: Publish blog posts.
Buffer MCP: Schedule social posts.

Claude can then handle an entire campaign:

“Create a full campaign for our new product launch, design visuals, write blog posts, and schedule social media content.”

The model triggers each MCP server in sequence, and you get a complete, multi-platform rollout—all from one prompt.

2. Developer Operations

For engineering teams, MCP servers can automate DevOps tasks:

GitLab MCP: Manage merge requests.
AWS MCP: Deploy builds.
Postgres MCP: Run health checks.

Example workflow:

“Deploy the latest stable branch to staging, run database migrations, and post deployment status to Slack.”

Claude orchestrates the entire process through MCP calls, logging everything through ToolHive.

3. Business Intelligence Dashboarding

With MCP connections to Salesforce, Google Sheets, and Tableau, Claude can compile daily or weekly reports automatically.

“Summarize last week’s sales performance by region and update the executive dashboard.”

The AI fetches data, runs calculations, and even updates visual dashboards—all autonomously.

MCP Security Model

Security is critical when your AI can take real-world actions. MCP enforces strict boundaries:

Scoped authentication: Each MCP server uses its own API credentials, never shared across services.
User consent: Each connection requires explicit user approval.
Action logging: Every tool invocation is logged and traceable.
Sandboxing: MCP servers can be isolated in namespaces or virtual networks.

In OpenShift, you can further harden security by using role-based access control (RBAC) and secrets management to protect credentials.

Example:

oc create secret generic github-credentials --from-literal=token=<your_token>

Then mount that secret into your MCP deployment as shown earlier.

Monitoring and Scaling MCP Deployments

Once you have multiple MCP servers running, you’ll want to ensure uptime and reliability.

Health Checks

Define readiness and liveness probes in your deployment spec:

livenessProbe:
  httpGet:
    path: /healthz
    port: 8080
  initialDelaySeconds: 15
  timeoutSeconds: 5
readinessProbe:
  httpGet:
    path: /ready
    port: 8080
  initialDelaySeconds: 5
  timeoutSeconds: 3

These ensure OpenShift automatically restarts unhealthy pods.

Version Control with ToolHive

ToolHive can manage multiple versions of the same MCP server. You can roll out updates gradually using canary deployments:

toolhive deploy mcp github-mcp --version v1.2.0 --strategy canary --traffic 20%

This lets you test new releases without disrupting production workflows.

Advanced Configuration: Chaining MCP Servers

One of the coolest aspects of MCP is tool chaining—allowing the model to combine multiple servers in a single reasoning sequence.

Example:

“Check my calendar for tomorrow’s client meetings, pull the latest project metrics from GitHub, and prepare a summary presentation in Google Slides.”

Behind the scenes, Claude may call:

Google Calendar MCP → fetch events.
GitHub MCP → retrieve project metrics.
Google Slides MCP → create a slide deck.

Each MCP server executes independently, returning JSON responses that the model then uses to inform the next step.

ToolHive lets you define composite workflows using YAML templates:

workflow:
  name: daily_briefing
  steps:
    - tool: calendar-mcp.get_events
      params: { date: today }
    - tool: github-mcp.get_repo_metrics
      params: { repo: org/project }
    - tool: slides-mcp.create_presentation
      params:
        title: 'Daily Briefing'
        content: '{{ previous_steps }}'

This kind of orchestration is where MCP truly shines—turning AI from a conversational interface into a contextual automation platform.

MCP Servers in the Claude Ecosystem

The ecosystem around Claude’s MCP implementation is expanding rapidly. As of early 2025, hundreds of servers are available:

Developer-focused: GitHub, GitLab, AWS, MongoDB, PostgreSQL.
Business-focused: Slack, Notion, Airtable, Salesforce, HubSpot.
Creative tools: Canva, Figma, YouTube, WordPress.
Metadata (name, version, author)
Server endpoint URL
Authentication method (OAuth, API key, etc.)
Supported tools (functions)

This plugin-like architecture is what’s fueling the next wave of AI automation. Instead of waiting for native integrations, teams can deploy or even build their own MCP servers and connect them instantly.

Building a Custom MCP Server

Sometimes you’ll want to integrate a niche internal system. Creating a custom MCP server is straightforward using the protocol’s open specification.

Here’s a minimal Python example using FastAPI:

from fastapi import FastAPI, Request
from pydantic import BaseModel

app = FastAPI()

class IssueRequest(BaseModel):
    repo: str
    title: str
    body: str

@app.post("/create_issue")
def create_issue(req: IssueRequest):
    # Simulated logic: create an issue in an internal tracker
    issue_url = f"https://tracker.local/{req.repo}/issues/{req.title.replace(' ', '-') }"
    return {"issue_url": issue_url}

@app.get("/schema")
def schema():
    return {
        "name": "internal-tracker-mcp",
        "tools": [{
            "name": "create_issue",
            "parameters": {"repo": "string", "title": "string", "body": "string"}
        }]
    }

Containerize it, deploy it on OpenShift, and register it via ToolHive—just like any other MCP.

ToolHive: The Control Center for MCPs

ToolHive acts as the orchestration and observability hub for MCP deployments. It provides:

Centralized MCP registry (discover, install, update servers).
Access control management (who can use what tool).
Metrics and logging (usage, latency, error rates).
Version management (rollbacks and canary deployments).

Developers can use the ToolHive CLI or UI to perform tasks like:

toolhive list mcps
toolhive describe mcp github-mcp
toolhive logs mcp github-mcp --tail 100

And yes—ToolHive itself exposes an MCP interface, meaning you can manage your MCPs via an MCP. That’s recursion at its best.

The Business Impact of MCP

For businesses, MCP doesn’t just mean technical convenience. It translates directly into productivity and ROI.

A team using Claude with MCP connections can:

Automate 70–80% of repetitive information work.
Reduce report generation time from hours to minutes.
Integrate AI into existing systems without major redevelopment.
Maintain full control of data and compliance boundaries.

The ROI numbers shared by early adopters are staggering—tens of thousands of dollars saved annually through automation.

More importantly, MCP lays the foundation for AI-native enterprises, where human creativity and machine execution operate in perfect sync.

Future Outlook

MCP is still early, but it’s evolving fast. Expect to see:

Standardization across vendors: OpenAI, Anthropic, and others are converging on compatible tool schemas.
Private MCP marketplaces: Enterprises will host internal directories for approved servers.
Policy-aware AI: Models that understand and respect enterprise compliance rules automatically.
Edge deployments: Lightweight MCP servers running on edge devices for real-time automation.

As this ecosystem matures, the line between “AI assistant” and “AI operator” will blur completely.

Conclusion

MCP servers are the unsung heroes of the new AI automation era. They bridge the gap between intelligence and action, transforming language models from conversational tools into operational platforms.

Deploying MCP servers on OpenShift with ToolHive is one of the most practical ways to bring this power into enterprise environments—securely, scalably, and with full observability. Whether you’re automating DevOps, streamlining business intelligence, or orchestrating creative workflows, MCP gives your AI the context and capability it needs to truly work.

If you haven’t already, start small: deploy your first MCP server, connect it to Claude, and experiment. Once you see what’s possible, you’ll never look at workflow automation the same way again.

If you want more deep dives like this, subscribe to our weekly newsletter AI Infra Today—where we decode the infrastructure behind the intelligence.