📘 Cloudamize MCP Server (End User User & Integration Guide)

🌐 Overview

The Cloudamize MCP Server is a containerized MCP gateway that exposes Cloudamize APIs to AI tools it connects Cloudamize to AI assistants, AI agents, and large-scale agentic systems through the Model Context Protocol (MCP).

It enables external systems to securely access and use Cloudamize data such as:

📦 Migration Plans

View migration plans, statuses, and provider-specific plan details.

Example Questions

• “What migration plans do I have for AWS?”

• “Show my active plans”

💰 Cost (TCO) Insights

Analyze migration costs and TCO summaries.

Example Questions

• “What is the total cost of my migration plan?”

• “TCO for plan 12345”

🖥 Infrastructure Insights

Explore discovered infrastructure, applications, and assessment data.

Example Questions

• “Summarize my infrastructure”

• “Show unmanaged servers”

• “What applications are running?”

💡 Workload Recommendations

Get cloud sizing and migration recommendations.

Example Questions

• “What instance recommendations do you suggest?”

• “Show AWS recommendations”

🧭 Migration Planning & Connectivity

Understand migration groups, waves, and server dependencies.

Example Questions

• “How are servers grouped for migration?”

• “Show server connectivity for Machine HOST-123”

Cloudamize MCP allows AI systems and agentic workflows to programmatically retrieve and act on Cloudamize assessment and migration data.

🔗 Hosted MCP Endpoint

https://mcp.cloudamize.com

🧠 Supported Consumers

Cloudamize MCP can be used by:

🤖 AI Assistants

• Cursor

• Claude Desktop

• IDE copilots

⚙️ AI Agents

• Custom automation agents

• Workflow agents

• Internal orchestration tools

🏗️ Agentic Systems (Enterprise Scale)

• AWS Transform

• Kiro agent

• Internal enterprise migration agents

• Multi-step autonomous workflows

• Cloud optimisation pipelines

💡 What MCP enables

AI systems can:

• Query Cloudamize assessment data

• Retrieve migration plans

• Generate cost and TCO insights

• Fetch workload recommendations

• Understand migration grouping strategies

• Use data inside multi-step automated workflows

🔁 Typical Usage Flow

1. System requests migration or assessment data

2. MCP server fetches Cloudamize data securely

3. Response is returned in structured format

4. AI agent/assistant uses it in reasoning or automation

🌐 Access Modes

❤️ Health check

GET /healthz

Response:

204 No Content

📡 MCP endpoint

POST https://mcp.cloudamize.com/

or local:

POST http://localhost:8080/

🔐 Authentication Methods

MCP currently supports Bearer Token:

Bearer Token

Authorization: Bearer <access_token>

🔑 Generate Access Token

#!/bin/sh

USERNAME="${API_USER:-your-email@email.com}"
PASSWORD="${API_PASS:-your password}"

URL="https://precloud-api.cloudamize.com/auth/token?termsAccepted=true"

if command -v base64 >/dev/null 2>&1; then
    AUTH=$(printf "%s:%s" "$USERNAME" "$PASSWORD" | base64)
else
    AUTH=$(printf "%s:%s" "$USERNAME" "$PASSWORD" | openssl base64)
fi
echo "Using user: $USERNAME"

# If Jq is not installed, use the following command to get the token
# curl --silent \
#   --location "$URL" \
#   --header "Authorization: Basic $AUTH"

  TOKEN=$(curl --silent \
  --location "$URL" \
  --header "Authorization: Basic $AUTH" \
  | jq -r '.access_token')

echo $TOKEN

Note: This script is currently only supporting for assessment's primary user credentials.

We can also pass x-api-key value in PASSWORD if we do not have credentials

⏳ Token Info

• Token validity: 12 hours

• Expired token → returns 401 Unauthorized

• Must be refreshed periodically

Connect to the Cloudamize MCP Server

Prerequisites

You need:

1. A Cloudamize API Bearer Token

2. An MCP-compatible client such as:

   • Cursor

   • Claude Desktop

   • AI Agents or Agentic Systems

Step 1 — Obtain Your Access Token

As mentioned above in Authentication Methods Section

Example token:

eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9...

Keep this token secure. It provides access to Cloudamize MCP tools and APIs.

Step 2 — Configure Your MCP Client

Add the Cloudamize MCP server to your MCP client configuration:

{
  "mcpServers": {
    "cloudamize": {
      "url": "https://mcp.cloudamize.com",
      "headers": {
        "Authorization": "Bearer $TOKEN"
      }
    }
  }
}

Replace:

$TOKEN

with your actual bearer token.

Step 3 — Restart Your MCP Client

After saving the configuration:

• Restart Cursor / Claude Desktop / your AI assistant

• The MCP client will automatically:

  • initialize the session

  • negotiate capabilities

  • discover available tools

No manual protocol calls are required.

Step 4 — Start Using Cloudamize Tools

You can now ask natural-language questions such as:

• “Show my infrastructure assessment”

• “Get migration recommendations”

• “Show observed infrastructure”

• “Fetch application dependency data”

• “Get server connectivity details”

⚠️ Troubleshooting

🔐 Security Principles

• Tokens must not be shared or embedded in code

• Do not store credentials in chat

• Use API keys or tokens securely

⚠️ Troubleshooting

🔐 Security

• Data stays within your Cloudamize account

• No external data is used

• Do not share tokens

• Do not store credentials in chat

• Use API keys or tokens securely

🧭 Decision Guide

👉 Use Hosted MCP if:

• You want zero setup

• Your AI tool supports remote MCP URLs

• You prefer managed infrastructure

👉 Use Local MCP if:

• Remote MCP is blocked

• You need offline / internal network support

• You want full control