cloud-finops

Cloud FinOps Skill & MCP

Open-source FinOps knowledge skill and MCP server for AI agents - Claude, ChatGPT, Gemini, Cursor, and any MCP-compatible client. Cloud cost optimisation across AWS, Azure, GCP and OCI, AI cost management and inference economics, Kubernetes, data platforms, allocation, chargeback, anomaly management, and named-pattern waste detection playbooks. Built by OptimNow, grounded in enterprise delivery experience.

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Install in 5 seconds

Full options, troubleshooting, and the model-agnostic API loader: see INSTALLATION.md. A version-tagged zip (cloud-finops-vX.Y.Z.zip) is attached to every GitHub release.

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What is a Skill? What is an MCP server?

A Skill is a structured knowledge folder you attach to an AI agent. Without it, general-purpose LLMs make confident but incorrect statements on FinOps topics - they miscalculate PTU break-even rates, confuse Azure and AWS reservation mechanics, and give advice that ignores how billing actually works. The answers sound plausible; they are wrong on the details that matter. The skill corrects that by injecting verified, curated FinOps knowledge directly into the model's context. The closest analogy is RAG, minus the infrastructure: no vector database, no embedding pipeline - you copy a folder and the model gains structured expertise. The same files work with Claude, GPT, Gemini, or any other model.

An MCP server (MCP: Model Context Protocol, the open standard that lets an AI client call external tools at run time) exposes the same library the other way round - as read-only tools the model queries on demand. Actual retrieval: one URL to paste, nothing to install.

Who this is for: FinOps practitioners building or evaluating AI-assisted cost analysis, cloud engineers who want a cost-aware assistant in their workflow, developers building internal FinOps agents, and Finance / IT managers evaluating the AI tooling their teams deploy. If you can copy a folder and follow the installation steps, you can use this.

Skill + installer, or MCP?

Same content, 2 delivery shapes - and they behave differently, because of how models use them (field-tested with the same battery of practitioner questions through both):

• Pushed into context - as a native skill (Claude Code, Claude.ai / Desktop, Kiro), or as rules files written by install.sh for tools without skill support (Cursor, Windsurf, Codex, Aider, Copilot, Gemini). The guidance is already there when the model reasons, with no per-question decision to make - which is why this surface grounds both advisory answers (commitment sizing, chargeback design, allocation methodology) and symptom questions ("my NAT gateway processes 10TB/month to S3"), where the measured probe runs show it handing over the matching runbook first try.
• Fetched on demand - the MCP server. The model must decide to call a tool per question, and that decision is this surface's real limit. Measured behaviour (August 2026 probe cycles): lookup and discovery questions ("show me the idle waste runbooks") route reliably; advisory and specific-symptom questions route on some phrasings and not others, and the tool descriptions name the questions each tool serves to move that probability, not to force the call. Its strengths: distribution (paste one URL - the right path for non-technical users and for hosts with neither skill support nor an installer target), faceted queries over the library's metadata, and interactive widgets on hosts that render MCP Apps (Claude.ai does not render them yet; see the MCP server section below).
• Both is legitimate. Skill loaded for the doctrine, connector added for faceted retrieval or for hosts where the skill is not loaded. Details on the 6 tools: [MCP server]