GitHub
Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.
Beschreibung vom Entwickler (englisches Original)
Cloud cost + FinOps knowledge for AI agents: AWS/Azure/GCP optimisation, AI spend, waste playbooks.
Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.
Für diesen Server haben wir noch keine Zusammenfassung in einfacher Sprache geschrieben. Unten stehen die Fakten, die wir aus der Registry gelesen haben.
Wähle die KI-App, die du nutzt, und kopiere die Einstellungen.
Du brauchst
Welche App nutzt du?
Wähle oben deine App. Wir merken sie uns, damit der Button beim nächsten Mal „In … installieren“ anzeigt.
Funktioniert in Claude im Web, auf dem Desktop und auf dem Handy:
https://mcp.optimnow.io/mcpÖffne Claude Desktop → Settings → Developer → Edit Config und füge dies in claude_desktop_config.json ein
{
"mcpServers": {
"cloud-finops": {
"command": "uvx",
"args": [
"cloud-finops-mcp"
]
}
}
}Führe das in deinem Terminal aus
claude mcp add --transport http cloud-finops https://mcp.optimnow.io/mcpclaude mcp add cloud-finops -- uvx cloud-finops-mcpIn .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"cloud-finops": {
"url": "https://mcp.optimnow.io/mcp"
}
}
}In .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"cloud-finops": {
"command": "uvx",
"args": [
"cloud-finops-mcp"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"cloud-finops": {
"type": "http",
"url": "https://mcp.optimnow.io/mcp"
}
}
}In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"cloud-finops": {
"type": "stdio",
"command": "uvx",
"args": [
"cloud-finops-mcp"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
ChatGPT verbindet sich nur mit Servern im Internet:
https://mcp.optimnow.io/mcpMenünamen können sich je nach Tarif und mit der Zeit leicht unterscheiden.
Deine Auswahl bleibt in einem Cookie auf diesem Gerät; kein Konto nötig.
Diese Einstellungen werden aus dem Registry-Eintrag des Entwicklers erzeugt. Wenn in der README etwas anderes steht, folge der README.
Tool-Namen und -Beschreibungen genau so, wie der Server sie am 6. Nov. 2026 gemeldet hat. Geschrieben hat sie der Entwickler, nicht wir; wir zeigen sie als reinen Text.
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
Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 6. Nov. 2026
Der Registry-Name wurde mit dem GitHub-Konto OptimNow bestätigt, dem das Repo gehört
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieses Repo haben wir noch nicht von GitHub gelesen
Antwort in 174 ms (HTTP 200)
Keine Anmeldung nötig
Wir haben 7 Tool-Beschreibungen gelesen und keine versteckten Anweisungen an die KI gefunden
Verglichen mit allen aktiven Servern dieser Kategorie in der Registry. Sterne zählen nur, wenn dem Herausgeber das Repo gehört.
Er hat 3 von 3 zutreffenden Basischecks bestanden. Den Code haben wir nicht gelesen. Bevor du einen Schlüssel oder Token hinzufügst, sieh nach, welche Zugriffe der Server verlangt, und nutze einen Schlüssel, der nur die Berechtigungen hat, die du brauchst.
Die Registry nennt keinen erforderlichen Schlüssel, manche Server bitten dich aber bei der ersten Nutzung, dich anzumelden.
Beides geht: Installiere ihn auf deinem Computer oder verbinde dich mit der Cloud-Version.
Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (1.47.0) kam gestern in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Er verbindet sich per URL, also funktionieren auch Apps, die Remote-MCP-Server akzeptieren, zum Beispiel ChatGPT im Entwicklermodus.
In derselben Kategorie: GitHub, Desktop Commander, Cloudflare MCP
Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.
So liest du eine Karte:★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti
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
Unverändert aus dem Repo des Entwicklers übernommen. PopNx MCP hat den Inhalt weder geprüft noch empfiehlt es ihn. HTML, Bilder und Skripte wurden entfernt. Links führen von dieser Website weg.
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:Nur der Anfang wird gezeigt; den Rest findest du im Repo