1. How to integrate CloudSpend with Claude using Zoho MCP
2. How to integrate CloudSpend with Cursor using Zoho MCP
3. How to integrate CloudSpend with VS Code using Zoho MCP
4. How to integrate CloudSpend with Windsurf using Zoho MCP
   

Why this is useful

Cloud cost analysis often involves navigating multiple dashboards, filters, and reports. That takes time and breaks your workflow. MCP changes that.

1. You can query CloudSpend data directly from your AI assistant.
2. No need to drill down into reports manually.
3. Faster access to insights across accounts, services, and resources.
4. Works inside tools your teams already use daily.
   

This is especially useful for finance teams, cloud engineers, and operations teams who need quick answers without context switching.

Benefits of MCP support

You can leverage the following benefits by using the Zoho MCP:

1. Faster analysis: Ask questions and get answers instantly instead of navigating multiple screens.
2. Natural language interaction: No need to remember report paths or filters. Just ask what you need.
3. Work from your existing tools: Use CloudSpend within Claude, VS Code, Cursor, or Windsurf.
4. Secure and controlled access: MCP supports both user-level authentication and shared access models.
5. Full data coverage: Access cost trends, anomalies, resource breakdowns, tagging details, and more across all modules.
   

Example scenarios

Investigating a cost spike

You ask: What caused the cost spike last week?

The assistant:

1. Pulls anomaly data and daily cost trends.
2. Identifies that a spike was detected on March 1.
3. Compares actual spend with expected baseline.
4. Highlights deviation and anomaly status.
   

1. Finds newly provisioned resources as the main driver.
2. Lists top contributors like Multi-AZ RDS instances and MemoryDB clusters.
3. Shows cost contribution per resource.

1. Total spend vs expected spend
2. Percentage deviation
3. Root cause explanation
4. Actionable recommendation

Checking total spend for the current month

1. Fetches spend data for the current billing cycle.
2. Uses available anomaly and cost analysis data.
   

1. Total AWS spend for March (MTD)
2. Expected baseline spend
3. Overages with percentage deviation
   
4. Current anomaly status
   

1. Explains why the spend is higher than forecast.
2. Links the increase to specific services like RDS and MemoryDB.
3. Gives a forward-looking note on how the bill may trend by month end.

Identifying the highest cost business unit

1. Aggregates cost data across all business units.
2. Ranks them based on month-to-date spend.
   

1. A ranked table of business units.
2. Cloud type, MTD spend, and trend vs last month.
3. Forecasted full-month spend.

1. Identifies the top spending business unit.
2. Shows whether its costs are increasing or decreasing.
3. Breaks down top cost drivers within that unit.
4. Account contribution.
5. Key services like CloudWatch.
6. Region-level spend.
   

Try it out