AI for FinOps — Cloud Cost Optimization with MCP

Open-source Model Context Protocol (MCP) resources for Cloud FinOps: cloud cost optimization, AI cost management, FinOps automation, tagging governance, cost anomaly detection, and budget monitoring — across AWS, Azure, and GCP.

What is this repository?

A practical resource hub for FinOps practitioners, cloud engineers, and platform teams who want to use AI agents to automate cloud cost optimization. This repo provides tutorials, MCP server documentation, client guides, and security frameworks for implementing the Model Context Protocol across AWS, Azure, and GCP.

18 MCP servers documented | 7 step-by-step tutorials | 9 MCP clients compared | 3 cloud providers covered

How do I get started?

Choose an MCP client — Claude, ChatGPT, Gemini, Copilot, Cursor, Kiro, VS Code
Run your first MCP tutorial — query real-time AWS pricing in 15 minutes
Explore all FinOps MCP servers — AWS, Azure, GCP, Tagging, JIRA, Slack

Recent Updates (May 2026)

AWS MCP Server GA (May 6, 2026) — Now part of the broader Agent Toolkit for AWS. The previous aws-mcp:InvokeMcp permission is replaced by standard IAM policies using the aws:ViaAWSMCPService and aws:CalledViaAWSMCP context keys. New run_script tool runs short Python scripts in a server-side sandbox for multi-step workflows. Frankfurt (eu-central-1) endpoint added alongside us-east-1. Updated: tutorial, server doc.
New: Workflow & Collaboration Servers — FinOps Tagging Compliance, JIRA, and Slack MCP servers for automated cost governance
Registry Backfill — registry.yaml now lists all 18 documented MCP servers
MCP Authentication Vulnerabilities (January 2026) — Critical security risks and remediation (read more)

What's inside?

Section	What you'll find
/foundations	What is MCP, how it works, architecture deep-dive
/servers	18 MCP servers — AWS, Azure, GCP, Tagging, JIRA, Slack
/clients	9 MCP clients compared — Claude, ChatGPT, Gemini, Copilot, Cursor, Kiro
/tutorials	7 step-by-step guides for AWS, Azure, GCP cost analysis
/governance	Security best practices, IAM policies, vulnerability guides

What is MCP and why does it matter for FinOps?

MCP connects AI clients to cloud cost management tools through a standardized protocol

The Model Context Protocol (MCP) is an open standard that lets AI agents securely connect to external tools — like AWS Cost Explorer, GCP BigQuery billing exports, Azure Cost Management, or third-party FinOps platforms like Vantage. It's the bridge between "ask a question about cloud spend" and "get a real answer from live data."

Industry Adoption (March 2026): MCP was donated to the Linux Foundation (December 2025) with backing from Anthropic, OpenAI, Google, Microsoft, and AWS. The ecosystem has grown to 10,000+ active MCP servers and is supported by all major AI platforms.

What can MCP do for cloud cost optimization?

Cost anomaly detection — AI agents query Cost Explorer and alert on spend spikes
Tagging compliance — automated audits of tag coverage and drift detection
Resource optimization — rightsizing recommendations, idle resource identification
Budget monitoring — real-time threshold alerts via Slack, JIRA ticket creation
Multi-cloud cost analysis — unified queries across AWS, Azure, and GCP
Cost simulation — what-if scenarios for commitment purchases and architecture changes

But MCP also raises governance and security challenges — this repo addresses both.

Contributing

We welcome contributions — new MCP servers, tutorials, use cases, and security guidance.

Fork the repo and create a branch
Add your content
Open a PR with a clear description

See CONTRIBUTING.md for details. New to MCP or FinOps? Start with issues labeled good first issue.

finops-mcp-resources