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Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.
Computadora o nube★ 34.245MITActualizado hace 3 días
Instalar
Cloudcostsmcp
Resumen
Descripción del creador (original en inglés)
Anchor AI FinOps to real, live cloud pricing — AWS, GCP & Azure public and enterprise rates.
Los tres pilares de PopNx MCP
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
- Opinión de usuariosiAún no hay reseñasLas reseñas se abrirán junto con las cuentas en una etapa posterior
- Nuestras revisiones básicasi1 de 1 superadasRevisado con información pública; el código aún no se ha escaneado
- UsoiAún sin medir★ –
Qué puedes hacer con él
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
- Conexión: Se ejecuta en tu computadora
- Transporte: stdio
- Categoría: Herramientas para desarrolladores
- Debes completar: OCC_GCP_API_KEY, AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY
Instalación
Elige la app de IA que usas y copia la configuración.
Antes de instalar
- Revisiones básicas: 1 de 1 superadas
- Se ejecuta en tu computadora con los permisos de tu usuario (archivos, red, apps).
- Debes completar tú:
OCC_GCP_API_KEY,OCC_AWS_ENABLE_COST_EXPLORER,AWS_ACCESS_KEY_ID,AWS_SECRET_ACCESS_KEY,OCC_GCP_BILLING_ACCOUNT_ID
Los valores marcados como secretos son llaves de tu cuenta. Nunca los compartas ni los pegues en un chat.
Necesitas
- PyPIuv de Python instalado (para el comando uvx)
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json
{
"mcpServers": {
"cloudcostsmcp": {
"command": "uvx",
"args": [
"opencloudcosts"
],
"env": {
"OCC_GCP_API_KEY": "your-value",
"OCC_AWS_ENABLE_COST_EXPLORER": "your-value",
"AWS_ACCESS_KEY_ID": "your-value",
"AWS_SECRET_ACCESS_KEY": "your-value",
"OCC_GCP_BILLING_ACCOUNT_ID": "your-value"
}
}
}
}Ejecuta esto en tu terminal
claude mcp add cloudcostsmcp --env OCC_GCP_API_KEY=your-value --env OCC_AWS_ENABLE_COST_EXPLORER=your-value --env AWS_ACCESS_KEY_ID=your-value --env AWS_SECRET_ACCESS_KEY=your-value --env OCC_GCP_BILLING_ACCOUNT_ID=your-value -- uvx opencloudcostsCompleta los valores vacíos antes de ejecutarlo.
O copia la configuración tú
Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"cloudcostsmcp": {
"command": "uvx",
"args": [
"opencloudcosts"
],
"env": {
"OCC_GCP_API_KEY": "your-value",
"OCC_AWS_ENABLE_COST_EXPLORER": "your-value",
"AWS_ACCESS_KEY_ID": "your-value",
"AWS_SECRET_ACCESS_KEY": "your-value",
"OCC_GCP_BILLING_ACCOUNT_ID": "your-value"
}
}
}
}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.
O copia la configuración tú
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"cloudcostsmcp": {
"type": "stdio",
"command": "uvx",
"args": [
"opencloudcosts"
],
"env": {
"OCC_GCP_API_KEY": "your-value",
"OCC_AWS_ENABLE_COST_EXPLORER": "your-value",
"AWS_ACCESS_KEY_ID": "your-value",
"AWS_SECRET_ACCESS_KEY": "your-value",
"OCC_GCP_BILLING_ACCOUNT_ID": "your-value"
}
}
}
}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.
Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
Valores que debes completar
- GCP free API key — required for GCP pricing, no billing account needed.
OCC_AWS_ENABLE_COST_EXPLORERenvSet to true to enable effective/discounted AWS pricing via Cost Explorer ($0.01/call).- AWS credentials — only needed for effective pricing (RI/Savings Plans). Public pricing works without credentials.
- AWS secret key — paired with AWS_ACCESS_KEY_ID for effective pricing.
OCC_GCP_BILLING_ACCOUNT_IDenvGCP billing account ID — enables effective/contract pricing via Cloud Billing API.
Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Qué puede hacer
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
Revisiones básicasi
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 6 nov 2026
- ✓ Superada
El creador coincide con el repo
El nombre en el registro se verificó con la cuenta de GitHub x7even, dueña del repo
- … Aún sin revisar
Sigue en mantenimiento
Todavía no hemos leído este repo en GitHub
- … Aún sin revisar
Tiene licencia
Todavía no hemos leído este repo en GitHub
- – No aplica
El endpoint responde
Este se ejecuta en tu computadora; no hay endpoint en internet
- – No aplica
Inicio de sesión con OAuth
Se ejecuta en tu computadora y usa ajustes locales en su lugar
- … Aún sin revisar
Sin instrucciones ocultas en las descripciones de herramientas
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
Comparado con otros en Herramientas para desarrolladoresi
- Más estrellas que el 0% de los otros 1390 servidores de esta categoría
- Publicado en el registro más recientemente que el 29% de los otros 1390 servidores de esta categoría
- Revisiones básicas iguales o mejores que el 100% de los otros 1390 servidores de esta categoría
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Preguntas frecuentes
¿Es seguro Cloudcostsmcp?
Superó 1 de 1 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.
¿Qué claves o códigos necesita?
Debes completar: OCC_GCP_API_KEY, AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY
¿Se ejecuta en mi computadora o en la nube?
Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).
¿Sigue en mantenimiento?
El repo de GitHub cambió por última vez –, y la última versión (1.0.2) llegó al registro hace 4 meses.
¿Qué apps pueden usarlo?
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Python (uv) en tu computadora.
¿Qué puedo usar en su lugar?
En la misma categoría: GitHub, Desktop Commander, Cloudflare MCP
Alternativas
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
Desktop Commander
de wonderwhy-er
MCP server for terminal commands, file operations, and process management
Se ejecuta en tu computadora★ 10.225MITActualizado hace 15 días
Instalar
Cloudflare MCP
de cloudflare
Cloudflare MCP servers
Se ejecuta en la nube★ 4446Apache-2.0Actualizado hace 14 meses
Instalar
README del creador
Abrir el README
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.
OpenCloudCosts MCP
Anchor AI FinOps to real, live cloud pricing.
Your LLM's cloud pricing knowledge was frozen at training cutoff. Cloud pricing wasn't.
opencloudcosts is an MCP server that gives your AI assistant live, structured access to AWS, GCP, and Azure pricing — 16 tools it can call directly, with results it can reason over rather than guess at. Ask it to compare a full multi-resource workload across all three clouds simultaneously. Ask it for your actual post-discount effective rates from Reserved Instances, Savings Plans, or Enterprise Discount Programs. Ask it to fan out across every region and return the cheapest option. None of that is possible from training data alone, and none of it is possible from a single-cloud pricing calculator.
The Problem
Ask any AI assistant what an m5.2xlarge costs in ap-southeast-2, whether AWS or GCP is cheaper for a three-tier web app, or what your effective hourly rate is after Savings Plans. You will get a confident answer. It will probably be wrong.
Cloud pricing is a poor fit for static model weights: instance families are added and retired, regional pricing diverges (the same instance type can differ 20–40% across regions), spot markets fluctuate, and commitment discounts are by definition unique to each account. A model answering from training data cannot know your negotiated EDP rate. It can barely reliably recall last year's list price. The problem is not that LLMs are bad — it's that real-time, account-specific pricing data is structurally outside what a model can know.
opencloudcosts fixes this by giving your AI assistant 16 MCP tools backed by live provider APIs. Instead of hallucinating numbers, the model calls a tool and gets a precise answer.
A Concrete Scenario
Your team asks: *"We're evaluating whether to migrate this workload from AWS to GCP. What does the full stack cost on-demand vs. three-year committed, across all three clouds, so I can make the case to leadership?"*
Without this tool, the model interpolates — possibly from pricing that is a year out of date — across three providers, multiple resource types, and at least four pricing tiers. The numbers will be plausible and wrong.
With opencloudcosts, the model calls compare_bom with the workload spec (compute + storage + database). The tool fans out 8 concurrent provider calls across AWS, GCP, and Azure simultaneously, prices each resource category at public and committed rates, and returns a per-provider, per-term breakdown with savings analysis — in a single tool call.
No spreadsheet. No switching between three provider calculators. No manual SKU matching. No training-data approximation.
Why Not the Obvious Alternatives
Asking a model without tools — Training data has a cutoff. Cloud pricing changes constantly, varies 20–40% across regions for the same instance type, and includes account-level commitment discounts that are invisible at inference time. The model will approximate, confabulate, or recall stale numbers. There is no structured output, and the model cannot access your contracted rates under any circumstances.
Cloud pricing calculators (AWS / GCP / Azure) — Each covers exactly one cloud. They are UI-only with no API surface callable from an AI assistant. Cross-cloud comparison requires manually reproducing the same architecture three times across three separate calculators and reconciling exports by hand. They have no concept of unit economics and produce no programmatic output for agentic workflows.
Infracost — Excellent at estimating cost diffs against Terraform plans. It requires IaC files as input — it cannot answer "what does an n2-standard-8 cost in europe-west4?" without a Terraform plan in hand. It is not an MCP server and is not callable from a conversational AI context.
Calling provider APIs directly — AWS bulk pricing files are multi-GB and require targeted API access patterns to avoid downloading them in full. GCP's Cloud Billing Pricing API v1beta requires a multi-source ADC credential chain and provider-specific IAM. Azure's Retail Prices API needs pagination logic and SKU matching. Every provider uses different region naming conventions, SKU formats, and data schemas. opencloudcosts normalizes all of this behind a uniform MCP tool interface and handles credential chains, caching, and retry logic so the model does not have to.
Capabilities at a Glance
| Capability | LLM (no tools) | Cloud Calculators | Infracost | opencloudcosts |
|---|---|---|---|---|
| Live pricing (not frozen at training cutoff) | No | Manual input only | IaC-bound | Yes — fetched from provider APIs |
| Cross-cloud comparison in one call | No | No | No | Yes — compare_bom, 8 concurrent provider calls |
| Effective/contracted rates (RI, SP, EDP, CUD) | No | No | No | Yes — credentials unlock this layer |
| Multi-region concurrent fan-out | No | No | No | Yes — up to 32 goroutines |
| Unit economics (cost/user, cost/request) | Approximate | No | No | Yes — estimate_unit_economics |
| MCP tool surface — callable by model | N/A | No | No | Yes — 16 tools |
| AWS + Azure public pricing, zero credentials | N/A | Single-cloud only | Partial | Yes |
| HTTP service for shared/Kubernetes deployments | N/A | N/A | No | Yes — bearer auth, rate limiting, probes |
Coverage: Three Clouds, 16 Tools
Fourteen tools are fully functional. Two entries are compatibility stubs: search_pricing (a deprecated redirect from v0.8.x, kept for backward compatibility) and get_spot_history (registered but not implemented in the Go server — returns a structured "not available" response).
Credential requirements by provider
| Coverage | Credentials required |
|---|---|
| AWS EC2, EBS (gp3/io2/sc1), RDS, inter-region egress | None |
| Azure VMs, Managed Disks, Blob Storage, Azure SQL/MySQL/PostgreSQL, Cosmos DB, AKS, Azure Functions, Azure OpenAI (GPT-4o, GPT-4, GPT-3.5-Turbo, o1, o1-mini, embeddings) | None — fully public Retail Prices API |
| GCP Compute Engine, Cloud Storage, Persistent Disk, Cloud SQL, Memorystore, GKE, BigQuery, Vertex AI, Gemini, Cloud LB/CDN/NAT/Armor, Cloud Monitoring | Free API key (OCC_GCP_API_KEY) — no billing account, no credit card |
AWS post-discount rates (Reserved Instances, Savings Plans) + get_discount_summary | AWS credentials + OCC_AWS_ENABLE_COST_EXPLORER=true ($0.01/call to Cost Explorer, opt-in only) |
| GCP committed-use discounts (CUDs) and Enterprise Discount Programs (EDPs) | ADC credentials + billing.billingAccountPrice.get IAM + OCC_GCP_BILLING_ACCOUNT_ID |
Azure Reserved VM pricing (1-year and 3-year terms) is available via the public Retail Prices API — no credentials needed. compare_bom returns committed-term Azure pricing with no setup beyond the binary.
The 16 tools by category
| Category | Tools |
|---|---|
| Pricing | get_price, get_prices_batch, compare_prices, describe_catalog, search_pricing†, get_spot_history† |
| FinOps | estimate_bom, estimate_unit_economics, compare_bom, get_discount_summary |
| Discovery | list_regions, list_instance_types, find_cheapest_region, find_available_regions |
| Cache | refresh_cache, cache_stats |
† Compatibility stub only — not functional for live data. See opencloudcosts-go/README.md for full parameter reference.
Performance and Reliability
Concurrency — The analysis tools are not sequential HTTP wrappers:
find_cheapest_regionandfind_available_regions: errgroup + semaphore, up to 32 goroutines — queries all available regions in parallel, returns results sorted cheapest-firstcompare_bom: 8 concurrent provider calls across AWS, GCP, and Azure simultaneouslycompare_prices: semaphore of 10 concurrent region callsget_prices_batch: parallelized across instance types within a region
Rate limiting and timeouts — Token-bucket rate limiter at 200 req/s on the HTTP transport (OCC_RATE_LIMIT). Per-request deadline: 60s (OCC_REQUEST_TIMEOUT). Per-provider API call: 30s (OCC_PROVIDER_TIMEOUT). Graceful SIGTERM drain: 15s (OCC_SHUTDOWN_TIMEOUT).
Cache — Prices are stored in a concurrent in-memory cache (read-optimised with sync.RWMutex) with atomic JSON persistence at ~/.cache/opencloudcosts/cache.json. TTLs: public prices 24h (OCC_CACHE_TTL_HOURS), region/instance metadata 7 days (OCC_METADATA_TTL_DAYS), effective/contracted rates 1h (OCC_EFFECTIVE_PRICE_TTL_HOURS). Cache survives binary updates. 401/403 responses from billing APIs are never cached, so credential rotation takes effect immediately.
AWS pricing — EC2/EBS/RDS public pricing uses a targeted SKU API path rather than downloading the full multi-GB bulk pricing file, keeping startup fast and avoiding large network payloads.
Error isolation — Raw exception text never reaches LLM context. All tool boundaries emit structured error envelopes; full tracebacks are logged server-side only. GCP contract pricing falls back to public list prices on auth failure rather than surfacing an error into the conversation.
Validated: 234/234
opencloudcosts v1.0.0 achieves 234/234 (100%) on the LLM grounding harness, with zero XML hallucinations across the full suite.
The harness covers 234 realistic pricing questions across all three clouds: instance spot checks, cross-region comparisons, BOM estimates, multi-cloud comparisons, unit economics, AI model pricing, database pricing, storage pricing, discount summaries, egress pricing, availability queries, and network pricing.
Primary validation model: `qwen3.6-35b-128k` running locally via llama-swap — a self-hosted 35B reasoning model with no external API dependency. The harness has also been exercised against qwen3.6-35b-a3b, qwen3.5-122b-a10b@q6_k, and gemma-4-26b-a4b. Because MCP is a protocol rather than a model feature, accuracy comes from the tool returning correct live data — any MCP-capable AI assistant calls the same tool surface and gets the same structured response.
Harness progression: 109/123 (Python v0.8.x) → 169/169 (v0.9.0) → 199/199 (v0.9.2) → 234/234 (v1.0.0).
643 Go unit tests across all providers and tools verify behavioral correctness and cross-provider API parity, independent of LLM evaluation.
Use Cases
1. Price the same workload across all three clouds at once
"Price the following on AWS, GCP, and Azure simultaneously: 4 instances (8 vCPU, 32 GB RAM), 1 managed PostgreSQL database (4 vCPU, 16 GB RAM), and 500 GB block storage. Return on-demand, 1-year committed, and 3-year committed totals for each cloud with monthly and annual figures, and flag which provider is cheapest at each commitment term."
compare_bom fans out 8 concurrent provider calls and returns a per-provider, per-term breakdown with savings analysis versus on-demand. No pricing calculator does this across cloud boundaries. AWS and Azure public pricing requires no credentials; GCP requires a free API key.
2. Find the cheapest AWS region for a long-running compute workload
"I need to run a c6a.4xlarge continuously. Fan out across all available AWS regions and return the 5 cheapest, sorted by on-demand hourly rate. Show us-east-1 as a baseline."
find_cheapest_region uses a 32-goroutine fan-out across every region where the instance type is available and returns results sorted cheapest-first. Regional price deltas for the same instance type routinely exceed 20%. No credentials needed.
3. Determine your effective AWS rate after commitments
"I h
Solo se muestra el principio; lee el resto en el repo