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Descripción del creador (original en inglés)
Cloud cost + FinOps knowledge for AI agents: AWS/Azure/GCP optimisation, AI spend, waste playbooks.
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
Elige la app de IA que usas y copia la configuración.
Necesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Funciona en Claude para web, escritorio y móvil:
https://mcp.optimnow.io/mcpAbre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json
{
"mcpServers": {
"cloud-finops": {
"command": "uvx",
"args": [
"cloud-finops-mcp"
]
}
}
}Ejecuta esto en tu terminal
claude mcp add --transport http cloud-finops https://mcp.optimnow.io/mcpclaude mcp add cloud-finops -- uvx cloud-finops-mcpPégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"cloud-finops": {
"url": "https://mcp.optimnow.io/mcp"
}
}
}Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"cloud-finops": {
"command": "uvx",
"args": [
"cloud-finops-mcp"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"cloud-finops": {
"type": "http",
"url": "https://mcp.optimnow.io/mcp"
}
}
}Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"cloud-finops": {
"type": "stdio",
"command": "uvx",
"args": [
"cloud-finops-mcp"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
ChatGPT solo se conecta a servidores en internet:
https://mcp.optimnow.io/mcpLos nombres de los menús pueden variar un poco según el plan y con el tiempo.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Nombres y descripciones de las herramientas tal como los informó el servidor el 6 nov 2026. Los escribió el creador, no nosotros; los mostramos como texto sin formato.
list_referencesSee 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 before deciding what to f
get_referenceFetch 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`
find_referencesFind 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" -
list_playbooksSee 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 alread
get_playbookFetch 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 Sa
find_playbooksFinds the runbook for a specific cloud waste pattern, filtered by provider, service, waste category or detection confidence. Useful before answering a specific waste or cost-fix question: each runbook includes a tested detection query the user can run in their own account, so it
get_live_figuresGet 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 g
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 6 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub OptimNow, dueña del repo
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
Respondió en 174 ms (HTTP 200)
No hace falta iniciar sesión
Leímos 7 descripciones de herramientas y no encontramos instrucciones ocultas para la IA
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Superó 3 de 3 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.
Funciona de las dos formas: instálalo en tu computadora o conéctate a la versión en la nube.
El repo de GitHub cambió por última vez –, y la última versión (1.47.0) llegó al registro ayer.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. Se conecta por URL, así que también funcionan las apps que aceptan servidores MCP remotos, como ChatGPT en modo desarrollador.
En la misma categoría: GitHub, Desktop Commander, Cloudflare MCP
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.
MCP server for terminal commands, file operations, and process management
Cloudflare MCP servers
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
FinOps knowledge for AI assistants: billing mechanics, commitment decisions and waste runbooks for AWS, Azure, GCP, AI and data platforms. By OptimNow.
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.
Each question below is answered by a file that exists in this repository today.
FinOps lead
Finance and procurement
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.
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.
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).
The matrix reads the library by business need. Every cell comes from a named reference file, listed in the notes under the tables.
| Level | Meaning |
|---|---|
| ● deep | A dedicated reference or section, with decision rules, worked examples or detection queries |
| ◐ partial | Covered inside a broader file, or mechanics only |
| – | Not covered today |
| n/a | The combination does not exist |
Hyperscalers
| Business need | AWS | Azure | GCP |
|---|---|---|---|
| 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 need | OCI | On-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 need | AI | Data | SaaS |
|---|---|---|---|
| Understand and allocate the bill | ● | ● | ◐ |
| Budget and forecast | ● | ◐ | ◐ |
| Commitments and discounts | ● | ● | ◐ |
| Contract negotiation and renewal | – | ◐ | ◐ |
| Reduce waste | ● | ● | ● |
| AI and GenAI spend | ● | ◐ | ● |
| Data platforms | n/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/.
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).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).finops-aws-commitments.md, finops-aws-patterns-databases.md (database commitment tree), playbooks/aws-expiring-commitment-no-decision.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).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.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).finops-bedrock.md, finops-aws.md (SageMaker), the GPU and SageMaker runbooks. Azure: finops-azure-openai.md. GCP:Solo se muestra el principio; lee el resto en el repo