Cloud FinOps Skill & MCP

io.github.OptimNow由 GitHub 账号 OptimNow 发布v1.49.1更新于 Nov 7, 2026

Cloud cost + FinOps knowledge for AI agents: AWS/Azure/GCP optimisation, AI spend, waste playbooks.

已验证Streamable HTTP可网页运行云端与基础设施金融知识与记忆

概览

AI 生成的概览

OptimNow 的云 FinOps 知识库，让助手查阅计费机制、承诺与分摊指导，以及云资源浪费处置手册。

可以做什么
  • 按 FinOps 能力、阶段、角色或成熟度发现并检索参考资料
  • 获取整份参考资料或其中某一章节，并附每条目的 token 估算
  • 按云厂商、服务、浪费类别或置信度发现并检索浪费处置手册
  • 获取单份手册，含症状、检测查询与修复步骤
  • 查询参考资料中带日期的数据所对应的当前数值
适用场景
适合回答云成本咨询类问题：该承诺多少用量、如何确定 Savings Plans 或预留实例规模、如何分摊或内部计费共享成本、如何做预测，或如何发现并修复某类具名浪费模式（如僵尸 NAT 网关、快照泛滥）。
运行要求
可使用托管远程端点（无需账号或认证），或使用 PyPI 包 cloud-finops-mcp 在本地以 Python 运行。未声明任何 API 密钥或环境变量。get_live_figures 工具会向 OptimNow 站点发起一次外部 HTTPS 请求。
安装前请注意
它无法访问你的云账号或账单；关于自有资源的问题，返回的是需你自行执行的检测查询。参考资料只讲计费机制而非当前价格，所引数值为示例并标注日期。使用托管连接器时，工具参数会发送至巴黎的服务器并写入日志，失败查询的参数也可能包含你问题中的词语。内容不构成法律、税务或会计建议。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Cloud FinOps Skill & MCP，将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Web executable，通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。

交给智能体安装

把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体，或 SourceWeft 对话中。智能体会读取该服务器的安装指南，先向你展示发布者、端点和登录方式，经你同意后再把它添加到你的客户端。密钥不会写进任何命令。

阅读 https://sourceweft.com/mcp/io.github.OptimNow%2Fcloud-finops/install.md，并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前，先告诉我它的发布者、端点和登录方式，等我确认。不要把 API 密钥写进命令或我会提交的文件；问我密钥应从哪里获取。

查看智能体所依据的安装指南

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。

{
  "mcpServers": {
    "cloud-finops-skill": {
      "type": "http",
      "url": "https://mcp.optimnow.io/mcp"
    }
  }
}

README

[OptimNow logo]

Cloud FinOps by OptimNow

FinOps knowledge for AI assistants: billing mechanics, commitment decisions and waste runbooks for AWS, Azure, GCP, AI and data platforms. By OptimNow.

[Listed in the Claude directory] [Latest release] [License: CC BY-SA 4.0]

[Install from the Claude directory]

Ask your assistant how to reduce a cloud bill, size a commitment, read a term sheet or allocate shared AI costs, and it answers from how billing works, with decision rules and worked examples, instead of general advice. The same files work in Claude, ChatGPT, Gemini, Cursor and any MCP-compatible tool. Open source, written by OptimNow from enterprise FinOps delivery, and aligned with the FinOps Foundation framework.

What you can ask, by role

Each question below is answered by a file that exists in this repository today.

FinOps lead

Finance and procurement

  • "Our AWS enterprise agreement renews in 6 months. What should we check on the term sheet before signing?" (finops-cloud-contracts.md)
  • "Finance wants a 12-month cloud forecast with an accuracy target. Which measures do we report, and how tight should the band be at our maturity?" (finops-forecasting.md)
  • "What has to be true in Finance and in the ERP before we move from showback to chargeback?" (finops-chargeback.md)

Engineering and platform

AI product owner

Neither the skill nor the connector can see your cloud account. For a question about your own resources, the answer is a detection query you run yourself, taken from the matching runbook.

What it covers

The FinOps Framework

The reference library is mapped to the 22 capabilities of the FinOps Foundation framework, grouped in its 4 domains. A filled square means at least one reference owns the capability; a grey square means the capability is covered inside another file; a dashed square is a known gap, listed with its reasoning in docs/ROADMAP.md.

[FinOps Framework capability coverage: 21 of 22 capabilities covered by the reference library]

This map is generated from the frontmatter of every reference file, and the CI pipeline fails when the committed map no longer matches the files (fcp-coverage.md).

Providers and business needs

The matrix reads the library by business need. Every cell comes from a named reference file, listed in the notes under the tables.

LevelMeaning
● deepA dedicated reference or section, with decision rules, worked examples or detection queries
◐ partialCovered inside a broader file, or mechanics only
–Not covered today
n/aThe combination does not exist

Hyperscalers

Business needAWSAzureGCP
Understand and allocate the bill●●●
Budget and forecast●●●
Commitments and discounts●●●
Contract negotiation and renewal●●●
Reduce waste●●●
AI and GenAI spend●●●
Data platforms◐●◐
SaaS and licences◐◐◐
Carbon footprint●◐◐

Other estates

Business needOCIOn-prem
Understand and allocate the bill◐●
Budget and forecast◐◐
Commitments and discounts◐◐
Contract negotiation and renewal––
Reduce waste◐◐
AI and GenAI spend–◐
Data platforms––
SaaS and licences–◐
Carbon footprint––

Platforms

Business needAIDataSaaS
Understand and allocate the bill●●◐
Budget and forecast●◐◐
Commitments and discounts●●◐
Contract negotiation and renewal–◐◐
Reduce waste●●●
AI and GenAI spend●◐●
Data platformsn/a●n/a
SaaS and licences◐◐●
Carbon footprint–––

On-prem covers owned data centres and the hybrid platforms (Outposts, Azure Local, Arc). AI covers the AI platforms: Anthropic, AWS Bedrock, Azure OpenAI, GCP Vertex AI and the open-weight vendor APIs. Data covers Databricks, Snowflake and Microsoft Fabric. SaaS covers SaaS applications, AI coding tools and licences.

Where each cell comes from

All paths are under plugins/cloud-finops/skills/cloud-finops/.

  • Understand and allocate the bill. AWS: references/finops-aws-billing.md, finops-allocation-showback.md, finops-focus.md, finops-tagging.md. Azure: finops-azure-billing.md, finops-focus.md, finops-tagging.md. GCP: finops-gcp.md (cost data foundation), finops-focus.md, finops-kubernetes.md. OCI, partial: finops-oci.md (cost reports, cost-tracking tags), finops-focus.md. On-prem: finops-on-prem-hybrid.md (internal bill, usage ingestion). AI platforms: the "Cost visibility and allocation" sections of finops-bedrock.md, finops-azure-openai.md, finops-vertexai.md, finops-anthropic.md; finops-for-ai.md. Data platforms: finops-databricks.md (system tables, allocation), finops-fabric.md (shared-capacity allocation), finops-snowflake.md (query attribution). SaaS, partial: finops-sam.md (discovery), finops-focus.md (FOCUS for SaaS vendors), finops-ai-dev-tools.md (attribution patterns).
  • Budget and forecast. AWS and Azure: finops-forecasting.md, finops-anomaly-management.md, and the Budgets sections of finops-aws-billing.md and finops-azure-billing.md. GCP: finops-gcp.md (budgets, spend caps), finops-anomaly-management.md. OCI, partial: finops-oci.md (Budgets). On-prem, partial: finops-on-prem-hybrid.md (rate setting); capacity planning is on the roadmap. AI platforms: finops-ai-value-management.md (AI investment budgets, stage gates), finops-forecasting.md (AI forecast scenarios), finops-anthropic.md (spend limits). Data platforms, partial: budget controls in finops-snowflake.md, finops-databricks.md, finops-fabric.md. SaaS, partial: finops-sam.md (renewal calendar, metrics), finops-ai-dev-tools.md (spending caps).
  • Commitments and discounts. AWS: finops-aws-commitments.md, finops-aws-patterns-databases.md (database commitment tree), playbooks/aws-expiring-commitment-no-decision.md, playbooks/aws-spot-candidate.md. Azure: finops-azure-commitments.md, playbooks/azure-unused-reservation.md. GCP: finops-gcp.md (CUDs, BigQuery commitments), finops-vertexai.md (Flexible Savings Plans), playbooks/gcp-cud-mismatch.md. OCI, partial: finops-oci.md (Universal Credits). On-prem, partial: finops-on-prem-hybrid.md (hybrid platform terms: Outposts, Exadata Cloud@Customer). AI platforms: finops-genai-capacity.md, provisioned capacity in finops-azure-openai.md, finops-bedrock.md, finops-vertexai.md. Data platforms: finops-databricks.md (committed-use bands), finops-snowflake.md (capacity model), finops-fabric.md (reservation). SaaS, partial: finops-ai-dev-tools.md (plan tiers), finops-sam.md (multi-year agreements).
  • Contract negotiation and renewal. AWS, Azure, GCP: finops-cloud-contracts.md, plus finops-azure-billing.md (EA-to-MCA). Data platforms, partial: the data platform section of finops-cloud-contracts.md, finops-databricks.md, finops-snowflake.md. SaaS, partial: finops-sam.md (renewal governance), finops-itam.md (pre-renewal checkpoint, joint negotiation). OCI, on-prem and AI platforms: no file. See "Contract negotiation and renewal" below.
  • Reduce waste. AWS: finops-aws.md, the 5 finops-aws-patterns-*.md files, 22 runbooks. Azure: finops-azure.md, finops-azure-patterns.md, 10 runbooks. GCP: the pattern catalogue inside finops-gcp.md, 7 runbooks. OCI, partial: finops-oci.md (6 patterns, no runbook). On-prem, partial: finops-on-prem-hybrid.md (idle capacity line, cost score). AI platforms: finops-for-ai.md (anti-patterns), finops-genai-capacity.md (provisioned-capacity waste), the optimisation sections of the 4 AI platform files, 2 cross-cloud runbooks. Data platforms: the pattern sections of finops-databricks.md, finops-snowflake.md, finops-fabric.md. SaaS: finops-sam.md (sprawl patterns), finops-ai-dev-tools.md (optimisation levers).
  • AI and GenAI spend. AWS: finops-bedrock.md, finops-aws.md (SageMaker), the GPU and SageMaker runbooks. Azure: finops-azure-openai.md. GCP: finops-vertexai.md. On-prem, partial: finops-ai-self-hosted-vs-managed.md. AI platforms: finops-for-ai.md, finops-agentic.md, finops-ai-value-management.md, finops-genai-capacity.md, finops-anthropic.md, finops-open-weight-vendors.md. Data platforms, partial: Cortex in finops-snowflake.md, model serving in finops-databricks.md. SaaS: finops-ai-dev-tools.md, playbooks/cross-cloud-coding-agent-token-waste.md.
  • Data platforms. AWS, partial: finops-databricks.md on AWS, Redshift commitments in finops-aws-patterns-databases.md. Azure: finops-fabric.md, finops-databricks.md. GCP, partial: BigQuery in finops-gcp.md. Data platforms: finops-databricks.md, finops-snowflake.md, finops-fabric.md.
  • SaaS and licences. AWS, Azure, GCP, partial: marketplace channel governance in finops-itam.md, Marketplace private offers in finops-cloud-contracts.md, Azure Hybrid Benefit in finops-azure-commitments.md. On-prem, partial: VMware core licensing in finops-on-prem-hybrid.md, BYOL in finops-itam.md. AI platforms, partial: licensing in finops-open-weight-vendors.md. Data platforms, partial: Pro, PPU and F-SKU licences in finops-fabric.md. SaaS: finops-sam.md, finops-itam.md, finops-ai-dev-tools.md.
  • Carbon footprint. AWS: greenops-cloud-carbon.md (Sustainability Console, Well-Architected pillar). Azure and GCP, partial: the same file, with the Azure and GCP depth pass on the roadmap; finops-gcp.md (Cloud Carbon Footprint).

Contract negotiation and renewal

The reference finops-cloud-contracts.md covers negotiated spend commitments with the 3 hyperscalers: AWS Private Pricing (EDP and PPA), Azure MACC and the Azure Commitment Discount, and Google Cloud Minimum Commitment. It describes the 12 clauses of an enterprise agreement, where the discount shows in billing data, migration funding (MAP, Azure Accelerate, RaMP), Marketplace private offers as a commitment lever, the reseller channel, 9 intake questions to ask before advising, a 15-point review grid for a term sheet, the usual pitfalls and the timing of a renewal. A closing section covers Databricks and Snowflake commitments inside a cloud contract. Benchmarks come from public sources named in the file; the library carries no observed contract figure.

Also covered: SaaS renewal governance (renewal calendar, auto-renewal review) in finops-sam.md and the pre-renewal checkpoint and marketplace channel governance in finops-itam.md.

Not covered today: negotiation with AI providers (Anthropic, OpenAI and the hosted model vendors; the library covers their reservation mechanics, not their enterprise agreements), OCI Universal Credits beyond their mechanics, and colocation or hosting contracts.

Waste runbooks

A runbook is a step-by-step procedure for one specific waste pattern: the symptoms, a detection query you run in your own account, the fix in safest-first order, and the mistake to avoid. The library files them by provider and by waste category, and each one carries a confidence level that says whether a single signal is enough to act.

[Waste runbook coverage: 43 runbooks across 8 waste categories and 4 scopes]

What it does not do

  • Live prices. The files carry billing mechanics, which stay true for years. Price figures go stale inside a packaged skill within weeks, so they are left out. For a current price the skill points to OptimToken, OptimNow's free comparison of LLM token rates and compute instance rates, each figure with its as-of date.
  • Your cloud account. Nothing here reads a bill or an API. For "which of my resources" questions, the deliverable is the runbook's detection query.
  • Legal, tax or accounting advice. The contract and chargeback references describe mechanics and the questions to ask; your counsel, tax adviser and auditors decide.
  • Topics still open. Accounting for owned capacity (depreciation, lease treatment), AI provider and OCI contract negotiation, and the FinOps Education & Enablement capability. Each is listed in docs/ROADMAP.md with the trigger that would reopen it.
  • Guaranteed routing. The model decides which file to read. When an answer arrives without a visible file read or tool call, ask for the library by name ("check the playbook library"). See docs/CONCEPTS.md.

Install

ToolOne-step install
[Claude Code]At the Claude Code prompt: /plugin marketplace add https://github.com/OptimNow/cloud-finops-skills.git then /plugin install cloud-finops@optimnow. The plugin is the skill only; for the 6 retrieval tools, add the hosted MCP connector separately (row below)
[Claude.ai / Claude Desktop]Install from the Claude directory (one click, kept current from this repository). Or download the latest release zip, then Settings -> Skills -> Upload zip
[ChatGPT]Self-host: ./install.sh --tool chatgpt --grouped (a public Cloud FinOps GPT is on the Roadmap)
[Gemini]Self-host: ./install.sh --tool gemini (a public Cloud FinOps Gem is on the Roadmap)
[Cursor] [Windsurf] [Codex] [Aider] [Copilot] [Kiro IDE] [Gemini CLI]One-liner: curl -sL https://raw.githubusercontent.com/OptimNow/cloud-finops-skills/main/install.sh | bash -s -- --tool <name>
[Auto-detect]curl -sL https://raw.githubusercontent.com/OptimNow/cloud-finops-skills/main/install.sh | bash
[MCP hosted]Nothing to install: claude mcp add --transport http cloud-finops https://mcp.optimnow.io/mcp. For Claude.ai / Desktop, Settings -> Connectors -> Add custom connector with exactly that URL (exactly that form, /mcp with no trailing slash - the widget sandbox domain is derived from it)
[MCP package]pip install cloud-finops-mcp then add to your MCP client config (Claude Code / Cursor / Codex / Windsurf / Cline). Snippets: ./install.sh --tool mcp

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.

How it works

  • The skill is a folder of markdown files: an entry point that routes a question to the right reference, the reference library and the runbooks. Installed in Claude, Kiro or through the installer for other tools, the guidance sits in the model's context before it answers. No code runs, no data leaves your machine.
  • The MCP connector serves the same library the other way round, as 6 read-only tools the model calls when it decides to: list, search by FinOps capability or waste category, and fetch one reference or runbook. One URL to paste (https://mcp.optimnow.io/mcp), nothing to install. Tool calls go to a server OptimNow runs in Paris; see "Data handling" below.
  • The plugin in the Claude directory is the skill only. The connector is a separate listing, so that the same content is not loaded twice in one session.
  • Live prices come from OptimToken, queried at answer time, never copied into these files.

The 2 delivery shapes behave differently: a loaded skill grounds advisory and symptom questions without a decision to make, while the connector depends on the model choosing to call a tool. What that means in practice, and when to use both, is in docs/CONCEPTS.md. Using a non-Claude model through an API, add the response contract from INSTALLATION.md to your system prompt so answers stay structured and billing-grounded.

Usage examples

More questions the library is written to answer, beyond the role list above:

  • "Our monthly bill jumped from $12K to $38K after a developer enabled Fast mode in Claude Code. How do I get this under control?"
  • "Should we self-host an open-weight model on rented H100s instead of paying per token, and what hidden costs do TCO calculators miss?"
  • "Our client wants separate AWS invoices per business unit. Their AWS contact suggested Cost Categories. Is that right?"
  • "We are migrating from EA to MCA. What FinOps work do we need to do before the switch?"
  • "We need to start reporting our cloud carbon emissions. Where do we begin?"
  • "Our Snowflake bill doubled in a quarter. Is it the warehouses, storage or Cortex, and what do we check first?"
  • "We run half our workloads in our own data centres. How do we give app teams a monthly bill for on-prem capacity they will accept?"

Design principles

  • Visibility before optimisation. Establish what you spend and what drives it, then act. The skill does not recommend commitments to a team that cannot allocate its bill.
  • Provider mechanics first, vendor claims second. Guidance is grounded in how billing works (CUR columns, Azure cost-management semantics, BigQuery export, FOCUS conformance), with primary sources cited. Vendor savings and sustainability claims are read critically.
  • Maturity is contextual. Where cloud is a cost centre, Crawl plus selective Walk is often the right state; where cloud is the product, Run pays for itself in gross margin. The skill assesses maturity before recommending.
  • Cost connects to business value. Every recommendation answers the CFO question: which business outcome does this protect or create.
  • Mechanics live here, price figures do not. Any figure a reference quotes carries its date and source; current prices route to OptimToken.
  • AI cost management is a first-class domain, with a reference per AI platform and a discipline file for agents, capacity and investment governance.

Data handling

What the skill and the hosted connector run, send and fetch, so you can decide where each is appropriate. Full policy in PRIVACY.md.

  • The skill is static text. The entry point, the references and the runbooks are markdown files read into the model's context. They run no code, call no network endpoint and carry no credentials. The runbooks contain detection queries (billing export SQL, CLI commands) that you run in your own cloud account; nothing in this repository reads a cloud account.
  • The hosted MCP connector, if you add it, sends tool calls to mcp.optimnow.io. When the model calls one of the 7 tools, the tool arguments (a reference or runbook name, a section phrase, facet filters, a guide slug) travel over HTTPS to that server and the matching content comes back. The server requires no account and no authentication, holds no database and no per-user state, and serves the same public files as this repository. Its application log writes one line per request (method, path, status and request type, never the arguments); when a lookup matches nothing, it also writes the tool name and the arguments that missed, which are short strings the model writes and can include a word copied from your question. The server runs on Fly.io, primary region Paris, which holds that log stream, possibly with the client IP address, under its default retention. OptimNow does not sell, share or profile from it.
  • Price lookups route to a live tool, not to this repo. Whether the model opens OptimToken is its decision in the conversation.
  • One tool makes an outbound request. get_live_figures sends one HTTPS GET to optimtoken.optimnow.io (an OptimNow site) for a guide's figures.json, carrying the guide slug and a cloud-finops-mcp User-Agent, nothing from the conversation. On the hosted connector the request leaves the server; with the PyPI package it leaves your machine. No other tool touches the network.
  • Nothing else. No telemetry, no analytics beacon, no update check, no package launcher, no credential read from your environment.

Maintenance

The library is updated weekly. Every Saturday an automated scan checks around 35 sources: cloud and AI provider release notes, billing changelogs and pricing pages, plus FinOps community publications. Each change is confirmed on the provider's own documentation and reviewed by a practitioner before it is applied. Every merge to main becomes a new plugin version in the Claude directory, and a version-tagged release ships the same content to PyPI and the MCP Registry. Watch or star the repository to be notified.

Contributing

Practitioner experience is the highest-value contribution. Frameworks and vendor docs are already public; what is rare is "we tried X in production, this is what it billed". Corrections to billing mechanics, new or improved runbooks, real-world counter-examples and adversarial review of the recommendations are all welcome: the repository is opinionated, and it should also be falsifiable.

The full guide, including the repository structure, contribution types, process and conventions, is in CONTRIBUTING.md. If your change names another OptimNow tool (an MCP tool name, an endpoint URL, a provenance field), read DEPENDENCIES.md first.

Adapting this skill for your organisation

Fork the repository and customise the reference files for your context: your cloud stack, your internal policies, your tag taxonomy, your negotiated rates. A fork gives you a stable base you can pull upstream updates into at your own pace. Typical customisations:

  • Adding organisation-specific tag requirements to finops-tagging.md
  • Replacing illustrative figures with your negotiated rates
  • Adding reference files for internal tools or platforms not covered here
  • Adjusting the methodology file to your team's own approach

About OptimNow

OptimNow is a boutique FinOps consultancy that helps organisations connect cloud and AI spend to measurable business value. Based in France, with European reach.

Open-source tools built by OptimNow:

ToolWhat it does
OptimTokenCompare what 250+ models cost per request, with caching and batch factored in, plus compute instance rates across 7 clouds. Also an MCP connector; this skill routes price questions here
AI ROI CalculatorWhether an AI project pays for itself: three-layer cost model, payback, break-even, sensitivity. Also an MCP server
AI Cost Readiness AssessmentWhere your organisation stands on AI cost management
MCP for TaggingTag governance automation

Acknowledgements

This skill incorporates content derived from the following sources:

  • FinOps Foundation: framework definitions, capability descriptions and maturity model structure are based on the FinOps Framework.
  • Tokenomics Foundation: the token complexity classes in the agentic FinOps reference are adapted from Big-T Notation by Dan Neff (Adobe), published by the Tokenomics Foundation under CC BY 4.0. The AI cost and value references also draw on the Foundation's five-layer tokenomics stack paper, its proposed definition of the total cost of AI, and the drafts of its consumption and value working groups, each cited where it is used.

Every other source is credited inline, in the file that uses it. Billing mechanics cite the provider's own documentation with the date it was read; figures and opinions from practitioner talks (FinOps X 2026 and others) are attributed to the speaker and organisation where they appear.

All referenced content has been adapted with additional context from OptimNow's consulting delivery experience. Any errors or opinionated interpretations are our own. This skill is independently maintained and is not affiliated with or endorsed by the FinOps Foundation.

License

Licensed under CC BY-SA 4.0. See LICENSE.md for the full licence text.

You are free to use, adapt and redistribute this skill, including for commercial purposes, as long as you credit OptimNow and share any derivatives under the same licence. When redistributing the material, please include this attribution:

Cloud FinOps Skill by OptimNow - licensed under CC BY-SA 4.0.

来源：README.md，提交 ec6fc26

工具

7

list_references

Browse the FinOps knowledge library

See what FinOps guidance is available: billing mechanics, commitment strategy, allocation and chargeback, AI cost management, and per-provider cost handbooks (AWS, Azure, GCP, OCI, Databricks, Snowflake, ...). Use this to discover what the library covers. When the question already names a FinOps domain, phase, persona or maturity, ``find_references`` returns only the matching references. Returns a dict shaped ``{"references": [...], "total": N}`` where each entry includes ``name``, ``title``, a one-line ``description``, the discriminating FCP facets (``fcp_domain``, ``fcp_capability``, ``fcp_phases``, ``fcp_personas_primary``, ``fcp_maturity_entry``) and ``approx_tokens``. ``approx_tokens`` gives each file's size before it is fetched: the library runs from about 2,000 to about 15,000 tokens per file, and ``get_reference(name, section=...)`` returns one section of a large one.

无需输入参数。

get_reference

Read one FinOps guide

Fetch the guidance on one FinOps topic - the billing mechanics, decision rules and worked examples behind a defensible answer - either whole or one section at a time. Use this when you need the actual content of one known reference, after ``list_references`` or ``find_references`` told you which one serves the question. Advisory questions (commitment sizing, chargeback design, allocation methodology) are served by the reference's decision rules and worked examples, which a summary does not carry. ``section`` is useful when the question is narrower than the file. The ``approx_tokens`` hint in the listing shows when this matters: the largest references run to about 15,000 tokens and the Azure pattern catalogue is an enumerated list, so a question about storage lifecycle is served by one section of ``finops-azure-patterns``. Without ``section`` the whole file comes back, with its cross-cutting reasoning. Args: name: Reference name as returned by ``list_references`` (e.g. ``"finops-aws"``, ``"finops-genai-capacity"``, ``"optimnow-methodology"``). section: Optional H2 or H3 heading to return on its own. Matched case-insensitively and partially against the headings, so a natural phrase works - ``"storage"``, ``"commitment decision tree"``. A heading's trailing count is ignored, so ``"storage optimization patterns"`` matches ``"Storage Optimization Patterns (28)"``. If it matches nothing you get the list of available headings back, not the whole file. Without ``section``, returns ``{"name": ..., "content": "...", "lines": N}`` where ``content`` is the file verbatim. With ``section``, returns ``{"name", "title", "section", "section_level", "partial": true, "content", "lines", "full_lines"}`` where ``content`` is that section prefixed by the reference's title, plus ``other_matching_sect

输入参数name必填section

find_references

Find the right FinOps guide

Find which guidance serves a FinOps question - how to commit, size, allocate, charge back, forecast, or govern cloud and AI spend. Use this for questions like "how should we size Savings Plans", "what should Finance own in chargeback", "what does a Crawl-stage org tackle first" - anything that maps to FinOps Framework facets (domain, capability, phase, persona, maturity) - and you want only the references that serve it, instead of scanning the full list. All filters are optional and combine with AND semantics. String matching is case-insensitive and exact (not substring). Examples: - ``find_references(domain="Optimize Usage & Cost")`` - ``find_references(phase="Optimize", persona="Engineering")`` - ``find_references(persona="Engineering", persona_primary_only=True)`` - ``find_references(capability="Rate Optimization")`` - ``find_references(maturity="Crawl")`` Args: domain: FinOps Framework domain (e.g. ``"Optimize Usage & Cost"``, ``"Quantify Business Value"``, ``"Manage the FinOps Practice"``). capability: FinOps capability (matches ``fcp_capability`` and ``fcp_capabilities_secondary``). phase: FinOps phase (``"Inform"``, ``"Optimize"``, ``"Operate"``). persona: Persona (matches ``fcp_personas_primary`` and ``fcp_personas_collaborating``). maturity: Entry maturity level (``"Crawl"``, ``"Walk"``, ``"Run"``). persona_primary_only: when True, ``persona`` matches only the primary list. Useful when the default match barely narrows the set - broad personas like Engineering collaborate on nearly every file, so filtering on collaboration is descriptive, not discriminating. ``persona="Engineering", persona_primary_only=True`` is the engineering reading list; the default is the everything-they-touch view. Returns ``{"filters": {...}, "references": [...], "to

输入参数phasedomainpersonamaturitycapabilitypersona_primary_only

list_playbooks

Browse the cloud waste runbooks

See every ready-made runbook for finding and fixing cloud waste: idle, orphaned and overprovisioned resources, egress surprises, schedule blindness and AI/ML inefficiency across AWS, Azure and GCP. Use this to discover which waste patterns have a runbook. When the question already names a provider, waste category, or confidence tier, ``find_playbooks`` returns only the matching runbooks. Each playbook is a small (~90-170 line) runbook scoped to one waste pattern (e.g. ``aws-zombie-nat-gateway``, ``azure-orphan-disks``). Returns ``{"playbooks": [...], "total": N}`` where each entry includes ``name``, ``title``, ``scope`` (aws/azure/gcp/cross-cloud), ``service``, ``waste_category``, ``confidence`` (obvious/likely/possible), and ``approx_tokens`` - the same size hint the reference listing carries, so a multi-playbook answer can be budgeted before fetching.

无需输入参数。

get_playbook

Read one waste runbook

Fetch the step-by-step runbook for one specific waste pattern: symptoms, the detection queries to run, the fix, and the anti-pattern to avoid. Use this when the user asks how to detect, confirm, or fix one specific named waste pattern (zombie NAT gateway, snapshot sprawl, idle SageMaker endpoint, ...), including questions about the user's own resources ("which of my X..."). Each runbook includes a detection query the user can run in their own cloud account; this connector has no access to the user's accounts. Args: name: Playbook slug as returned by ``list_playbooks`` (e.g. ``"aws-zombie-nat-gateway"``, ``"azure-orphan-disks"``, ``"cross-cloud-untagged-spend-drift"``). Returns ``{"name": ..., "title": ..., "content": "...", "lines": N}``. On miss, returns ``{"error": ..., "suggestions": [...]}`` with up to three string-distance matches so the caller can self-correct. A host with MCP Apps (SEP-1865) support may render this result via the linked ``ui://cloud-finops/playbook-viewer`` resource instead of showing the raw markdown.

输入参数name必填

find_playbooks

Find a waste runbook

Finds runbooks for a suspected cloud-waste pattern, filterable by provider, service, waste category or detection confidence. Useful when the user asks how to detect or fix a specific waste pattern; each runbook includes a detection query the user can run in their own account. This connector has no access to the user's accounts. Use this for questions like "which VMs are running for nothing", "why is our NAT bill so high", "my NAT gateway processes 10TB to S3", "should I delete these old snapshots", "which of our RIs are about to expire", "what waste can we clean up safely without review" - anything that names a provider, a waste category, or how confident the detection needs to be before acting. Patterns covered include NAT gateways and VPC endpoints, expiring Savings Plans / RIs / reservations, snapshot sprawl, S3 lifecycle gaps, idle or stopped VMs, orphaned disks / public IPs / EBS volumes, GPU and SageMaker sizing, Kubernetes idle capacity, and schedule blindness. All filters are optional and combine with AND semantics. String matching is case-insensitive and exact. Examples: - ``find_playbooks(scope="aws")`` - all AWS-specific playbooks - ``find_playbooks(waste_category="idle")`` - every idle-resource pattern - ``find_playbooks(scope="cross-cloud", confidence="obvious")`` Args: scope: ``"aws"``, ``"azure"``, ``"gcp"``, or ``"cross-cloud"``. service: Provider service exact-match (e.g. ``"AWS NAT Gateway"``). waste_category: ``"orphaned"``, ``"idle"``, ``"overprovisioned"``, ``"commitment-mismatch"``, ``"schedule-blindness"``, ``"modernization"``, ``"ai-ml-inefficiency"``, or ``"egress"``. confidence: ``"obvious"`` (single signal is enough), ``"likely"`` (two signals required), or ``"possible"`` (needs human review). From the OptimNow three-tier confidence model in `finops-waste-detection-p

输入参数scopeserviceconfidencewaste_category

get_live_figures

Current figures from an OptimToken guide

Get the current value of a figure a reference quotes with a date. Use this when a reference quotes a figure from an OptimToken guide ("measured on the 2026-10-25 OptimToken catalogue") and the answer needs the current value: the Compute Savings Plan or reservation discount, the gap between a flexible and a locked commitment, the ARM discount against Intel and AMD, the share of LLM models with a batch rate. It fetches the guide's figures file, rebuilt daily, and returns it as published. Args: guide: The guide slug, lowercase letters, digits and hyphens. Guides with figures today: ``"savings-plans-vs-reserved-instances"``, ``"arm-against-x86"``, ``"batch-api-economics"``. Returns ``{"document": {...}, "fetched_at": ..., "source": "live" | "cached", "note": ...}``, plus ``cache_age_seconds`` when cached (six-hour cache). ``document`` is the file unchanged: ``guide``, ``title``, ``url``, ``dataSource``, ``snapshotDate`` (the catalogue the figures come from), ``dateModified`` and ``figures``, where discounts are fractions (0.2743 is 27.4%) and each key names a median, a range or a count. An unknown slug returns ``{"error", "available_guides", "suggestions"}``. Any other failure (timeout, HTTP error, invalid JSON) returns ``{"error", "fallback"}``: the dated figure in the reference then stands, quoted with its date.

输入参数guide必填

版本历史

14
  1. v1.49.1最新Nov 7, 2026
  2. v1.47.0Nov 5, 2026
  3. v1.46.0Oct 31, 2026
  4. v1.45.0Oct 25, 2026
  5. v1.44.0Oct 25, 2026
  6. v1.43.0Oct 20, 2026
  7. v1.42.0Oct 13, 2026
  8. v1.41.1Oct 13, 2026
  9. v1.41.0Oct 11, 2026
  10. v1.40.1Oct 2, 2026
  11. v1.40.0Oct 1, 2026
  12. v1.39.0Oct 1, 2026
  13. v1.37.0Sep 26, 2026
  14. v1.36.0Sep 16, 2026

评分

已满足适用检查项的 100%

该评分衡量此收录条目的完整程度，依据注册中心条目、端点与公开来源计算得出。它不是对服务器本身的评价，也不表示服务器安全或当前可用：可连通性是带日期的检查结果，评分也不包含安装量或用户评价。

  • 近 30 天内可连通必需25 分
  • 已声明认证方式必需10 分
  • 已列出工具必需15 分
  • 描述必需15 分
  • 图标10 分
  • 文档15 分
  • 许可证10 分