io.github.OptimNow/ai-pricing-hub

Est-ce que io.github.OptimNow/ai-pricing-hub est sûr ?

Au dernier audit MCP Hub, le serveur MCP « io.github.OptimNow/ai-pricing-hub » est classé « Non encore testé ». Référencé dans l'annuaire. Seul le pentest comportemental fait foi, et il n'a pas encore été exécuté sur ce serveur. Le verdict est réévalué en continu ; consultez la fiche pour l'état à jour.

Méthode : MCP Hub analyse la description de chaque outil (couche 1) et, pour le verdict fort, exécute le serveur dans une micro-VM isolée avec leurres et réseau bloqué (couche 2). On publie des noms et des compteurs, jamais le contenu brut renvoyé par un outil.

Les 5 outils

• compare-compute-pricing≈ 1351 tokens

  Compare cloud compute instance pricing across AWS, Azure, GCP, DigitalOcean, OCI, OVH, and Alibaba. Filter by region, provider, vCPUs, memory, category, processor, or use case. All prices are Linux on-demand list prices in USD. Not every price column is live: `provenance.priceTypes` says which of each provider's price columns come from a live API, which are static constants, and which are unavailable, and `provenance.staticPriceColumns` lists the non-live ones outright; a savings-plan or reserved rate that appears there is a static estimate, not a live quote. Prices are returned as the catalogue serves them and dated by `provenance`.

  Paramètres, retour & exemple (11)

  Paramètres

  • limit · number · optionnel
    Max instances to return (default: 20)
  • region · string · optionnel · us-east | us-west | europe | asia-pacific
    Pricing region: us-east, us-west, europe, asia-pacific. Default: us-east
  • sortBy · string · optionnel · price | vcpus | memory | pricePerVCPU
    Sort by: price, vcpus, memory, pricePerVCPU. Default: price
  • useCase · string · optionnel
    Use case. Must be one of these exactly (case-insensitive): Web App, Database, HPC, ML & AI, Dev/Test, Big Data. Any other string returns zero matches rather than an error.
  • category · string · optionnel
    Instance category. Must be one of these exactly (case-insensitive): General Purpose, Compute Optimized, Memory Optimized, Storage Optimized, GPU / Accelerated, Burstable. Any other string returns zero matches rather than an error.
  • maxVCPUs · number · optionnel
    Maximum number of vCPUs
  • minVCPUs · number · optionnel
    Minimum number of vCPUs
  • provider · string · optionnel
    Cloud provider. Must be one of these exactly (case-insensitive): AWS, Azure, GCP, DigitalOcean, OCI, OVH, Alibaba. Any other string returns zero matches rather than an error.
  • maxMemory · number · optionnel
    Maximum memory in GiB
  • minMemory · number · optionnel
    Minimum memory in GiB
  • processor · string · optionnel
    Processor. Matched by exact equality (case-insensitive), so a partial value like 'H100' returns nothing. Known values: Intel, AMD, Graviton, Ampere, NVIDIA A100, NVIDIA H100, NVIDIA L4, NVIDIA T4, NVIDIA V100, NVIDIA Other.

  Exemple d'appel

  {"name": "compare-compute-pricing", "arguments": {"limit": 1, "region": "us-east", "sortBy": "price", "useCase": "texte", "category": "texte", "maxVCPUs": 1}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"error": "texte", "source": "texte", "instances": [], "provenance": {}, "catalogSize": 1, "matchingCount": 1}}

• compare-llm-models≈ 1558 tokens

  Browse and filter the whole LLM catalogue and get back a ranked table: price, quality (ELO), efficiency and capabilities. Use this when the user wants to SEE THE FIELD — 'show me models under $1/1M', 'which providers have vision models', 'list open-weight models above ELO 1300'. For a single PICK under a budget use recommend-llm-model; to weigh 2-4 NAMED models against each other use compare-models-side-by-side. Prices come from optimtoken.optimnow.io where reachable; the response's `provenance` says which tier served them and whether they are vendor-verified. Filter by provider, price tier (category), openness, capability, price range, or minimum ELO score. Optionally enrich with business metrics for a use case. Price tier and openness are independent: a model can be Frontier-priced and open-weight at once. Reports both list-price cost and the optimized cost achievable with prompt caching and the batch API. Prices, costs and scores are final as returned: rounded and ranked server-side, and dated by `provenance`. The text block is a numbered table of the same figures.

  Paramètres, retour & exemple (10)

  Paramètres

  • limit · number · optionnel
    Max models to return (default: 15)
  • minElo · number · optionnel
    Minimum Chatbot Arena ELO score. Typical range 1000-1500; ~1400 is roughly frontier-class. Models with no ELO score never satisfy this.
  • category · string · optionnel
    Price tier, matched by exact equality (case-insensitive): Frontier, Mid-tier, Budget, Image. This is cost only — self-hostability is the separate `openness` axis.
  • openness · string · optionnel · Open source | Open weights | Proprietary | Unknown
    Filter by self-hostability, derived from the licence: Open source, Open weights, Proprietary, Unknown
  • provider · string · optionnel
    Filter by provider name (e.g. 'OpenAI', 'Anthropic', 'Google')
  • capability · string · optionnel
    Capability, matched by exact equality (case-insensitive): Text, Vision, Reasoning, Agents, Image Gen, Audio. Any other string returns zero matches. There is no Code capability: for what coding costs, use useCasePreset codingTask.
  • volumePreset · string · optionnel · 10k | 100k | 1m
    Monthly request volume: 10k, 100k, or 1m. Default: 100k
  • maxInputPrice · number · optionnel
    Max input price per 1M tokens in USD
  • useCasePreset · string · optionnel · supportTicket | knowledgeQA | meetingSummary | marketingContent | codingTask | invoiceProcessing | callSummary | agentWorkflow
    Workload shape, which sets tokens per request: supportTicket (1.5k in / 500 out), knowledgeQA (2k / 800), meetingSummary (10k / 1.2k, batch-eligible), marketingContent (2.5k / 1.8k), codingTask (3k / 2k), invoiceProcessing (1.5k / 600, batch-eligible), callSummary (2k / 700, batch-eligible), agentWorkflow (6k / 3k). Default: supportTicket
  • maxOutputPrice · number · optionnel
    Max output price per 1M tokens in USD

  Exemple d'appel

  {"name": "compare-llm-models", "arguments": {"limit": 1, "minElo": 1, "category": "texte", "openness": "Open source", "provider": "texte", "capability": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"error": "texte", "models": [], "source": "texte", "eloAsOf": "texte", "dataAsOf": "texte", "provenance": {}, "catalogSize": 1, "finopsBadge": {}, "volumeLabel": "texte", "useCaseLabel": "texte", "matchingCount": 1}}

• compare-models-side-by-side≈ 1070 tokens

  Compare 2-4 named LLM models against all 8 use-case profiles at a chosen monthly volume, showing list and optimized cost for each. Use when the user names specific models to weigh against each other, rather than filtering the whole catalogue. If they also supply their own token counts, or a volume outside 10k/100k/1m, use estimate-llm-cost instead. Every name is resolved against the catalogue and the outcome is reported in `resolution` and in the text: a name that matched nothing, matched several models, or duplicated an earlier pick is stated explicitly, and such a name gets no column rather than a zero cost. Prices and costs are final as returned: rounded server-side and dated by `provenance`.

  Paramètres, retour & exemple (2)

  Paramètres

  • models · array · requis
    2-4 model names to compare, e.g. ['GPT-4o', 'Claude Opus 5', 'Gemini 3.1 Pro']
  • volumePreset · string · optionnel · 10k | 100k | 1m
    Monthly request volume: 10k, 100k, or 1m. Default: 100k

  Exemple d'appel

  {"name": "compare-models-side-by-side", "arguments": {"models": []}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"error": "texte", "models": [], "source": "texte", "volume": 1, "eloAsOf": "texte", "dataAsOf": "texte", "provenance": {}, "resolution": [], "volumeLabel": "texte"}}

• estimate-llm-cost≈ 1284 tokens

  Cost a workload with EXACT numbers the caller supplies: arbitrary token counts per request and any monthly volume, not just the 10k/100k/1m presets the other cost tools use. Use this for 'about 800 in and 200 out, 4 million calls a month', or to price one named model across every use-case profile. To compare 2-4 named models like for like at a preset volume, use compare-models-side-by-side instead. Provide a model name to get detailed cost breakdowns, or compare costs across all use case presets. Each figure comes twice: list price, and the optimized price achievable with prompt caching and the batch API. Cost figures are final as returned: rounded server-side and dated by `provenance`, with the lever behind each saving, or the reason there is none, named in the text.

  Paramètres, retour & exemple (5)

  Paramètres

  • modelName · string · optionnel
    Model name, e.g. 'GPT-4o'. A partial name matches up to 5 models and ALL of them are costed. If omitted, the first 8 catalogue entries are used — that is catalogue order, not a quality ranking.
  • monthlyVolume · integer · optionnel
    Exact monthly request count, any integer (default 100,000). This tool does not take the 10k/100k/1m presets the other cost tools use.
  • useCasePreset · string · optionnel · supportTicket | knowledgeQA | meetingSummary | marketingContent | codingTask | invoiceProcessing | callSummary | agentWorkflow
    Workload shape, which sets tokens per request: supportTicket (1.5k in / 500 out), knowledgeQA (2k / 800), meetingSummary (10k / 1.2k, batch-eligible), marketingContent (2.5k / 1.8k), codingTask (3k / 2k), invoiceProcessing (1.5k / 600, batch-eligible), callSummary (2k / 700, batch-eligible), agentWorkflow (6k / 3k). Default: every preset.
  • customInputTokens · integer · optionnel
    Custom input tokens per request. Must be supplied TOGETHER with customOutputTokens — either alone is ignored and the preset is used. A custom shape assumes no cacheable prefix and no batch eligibility, so its optimized cost equals its list cost.
  • customOutputTokens · integer · optionnel
    Custom output tokens per request. Must be supplied TOGETHER with customInputTokens — either alone is ignored and the preset is used.

  Exemple d'appel

  {"name": "estimate-llm-cost", "arguments": {"modelName": "texte", "monthlyVolume": 1, "useCasePreset": "supportTicket", "customInputTokens": 1, "customOutputTokens": 1}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"error": "texte", "source": "texte", "volume": 1, "eloAsOf": "texte", "dataAsOf": "texte", "modelCosts": [], "provenance": {}}}

• recommend-llm-model≈ 1791 tokens

  Pick a model. Returns a ranked top 3 for one workload under optional constraints, each with a per-constraint satisfied/violated breakdown as the evidence. Use this when the user wants an ANSWER rather than a table — 'what should I use for support tickets under $500 a month'. To browse or filter the whole catalogue instead, use compare-llm-models. Constraints: (monthly budget, minimum ELO, required capability, self-hostability). Returns a top 3 as structured facts — efficiency rank, ELO, list and optimized cost, FinOps flag, volatility, and a per-constraint satisfied/violated breakdown. When nothing satisfies every constraint the query is reported as over-constrained, `recommendations` is empty, and the nearest misses are returned in `nearMisses` instead, each carrying the constraint it failed and flagged as a miss in the text. The ranking is computed server-side and is final.

  Paramètres, retour & exemple (6)

  Paramètres

  • minElo · number · optionnel
    Minimum Chatbot Arena ELO score. Typical range 1000-1500; ~1400 is roughly frontier-class. Models with no ELO score never satisfy this.
  • openness · string · optionnel · Open source | Open weights | Proprietary | Unknown
    Require a self-hostability bucket, derived from the licence: Open source, Open weights, Proprietary, Unknown
  • volumePreset · string · optionnel · 10k | 100k | 1m
    Monthly request volume: 10k, 100k, or 1m. Default: 100k
  • useCasePreset · string · requis · supportTicket | knowledgeQA | meetingSummary | marketingContent | codingTask | invoiceProcessing | callSummary | agentWorkflow
    Workload shape, which sets tokens per request: supportTicket (1.5k in / 500 out), knowledgeQA (2k / 800), meetingSummary (10k / 1.2k, batch-eligible), marketingContent (2.5k / 1.8k), codingTask (3k / 2k), invoiceProcessing (1.5k / 600, batch-eligible), callSummary (2k / 700, batch-eligible), agentWorkflow (6k / 3k).
  • maxMonthlyBudget · number · optionnel
    Maximum monthly budget in USD at the given volume. Tested against the LIST-price monthly cost, not the caching/batch-optimized cost.
  • requiredCapability · string · optionnel
    Capability the model must have: Text, Vision, Reasoning, Agents, Image Gen, Audio. There is no Code capability: for coding, pass useCasePreset codingTask.

  Exemple d'appel

  {"name": "recommend-llm-model", "arguments": {"useCasePreset": "supportTicket"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"error": "texte", "source": "texte", "volume": 1, "eloAsOf": "texte", "dataAsOf": "texte", "nearMisses": [], "provenance": {}, "catalogSize": 1, "rankedCount": 1, "volumeLabel": "texte", "useCaseLabel": "texte", "candidateCount": 1}}

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Source

https://optimtoken-mcp.optimnow.io/mcp