ATOM Pricing Intelligence

{
  "id": "aix_da62acc440",
  "name": "ATOM Pricing Intelligence",
  "operator": "(unclaimed - source: pulsemcp · publisher: github.com)",
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  "last_crawled": "2026-08-13",
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    "pricing",
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  "skills": [
    "general-tools"
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  "protocols": {
    "mcp": "https://atom-mcp-server-production.up.railway.app/mcp",
    "a2a": null
  },
  "pricing": null,
  "regions": [
    "global"
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  "languages": [
    "en"
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  "verification": {
    "identity": "none",
    "health": "liveness-only",
    "pricing": "unknown",
    "last_check": "2026-08-13T04:01:42.485Z"
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  "pricing_model": "unknown",
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      "url": "https://www.pulsemcp.com/servers/a7om-pricing-intelligence"
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      "url": "https://github.com/a7om-ai/atom-mcp-server"
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    {
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      "url": "https://a7om.com/mcp"
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  "public_stats": {
    "npm_downloads": 856
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  "unreachable": false,
  "profile": {
    "mcp_server": "atom-mcp-server",
    "mcp_version": "1.1.0",
    "tool_count": 9,
    "tools": [
      {
        "name": "search_models",
        "description": "Search and filter AI inference models across all tracked vendors and SKUs.\n\nQuery by modality (Text, Image, Audio, Video, Multimodal), vendor, creator, model family, open-source status, price range, context window, and parameter count.\n\nReturns matching models with pricing. Free tier shows count + price range; paid tier shows full details.\n\nExamples:\n  - \"Find open-source text models under $1/M tokens\" → open_source=true, modality=\"Text\", max_price=0.001\n  - \"What multimodal models does Google offer?\" → vendor=\"Google\", modality=\"Multimodal\"\n  - \"Models with 128K+ context window\" → min_context_window=128000"
      },
      {
        "name": "get_model_detail",
        "description": "Deep dive on a single AI model: technical specs + pricing across all vendors.\n\nReturns model_registry data (context window, parameters, open-source status, training cutoff, model family) plus all SKU pricing across every vendor that offers this model.\n\nExamples:\n  - \"Tell me everything about GPT-4o\" → model_name=\"GPT-4o\"\n  - \"Claude Sonnet 4.5 specs and pricing\" → model_name=\"Claude Sonnet 4.5\""
      },
      {
        "name": "compare_prices",
        "description": "Cross-vendor price comparison for a specific model or model family.\n\nShows the same model (or family) priced across different vendors, sorted cheapest first. Essential for cost optimization and vendor selection.\n\nExamples:\n  - \"Compare Llama 3.1 70B pricing across vendors\" → model_name=\"Llama 3.1 70B\"\n  - \"Cheapest GPT-4 family output pricing\" → model_family=\"GPT-4\", direction=\"Output\"\n  - \"Claude pricing comparison\" → model_family=\"Claude\""
      },
      {
        "name": "get_vendor_catalog",
        "description": "Full catalog for a specific vendor: all models, modalities, and pricing.\n\nReturns vendor metadata (country, region, pricing page URL) plus every model and SKU they offer.\n\nExamples:\n  - \"What does Together AI sell?\" → vendor=\"Together AI\"\n  - \"OpenAI's text model pricing\" → vendor=\"OpenAI\", modality=\"Text\"\n  - \"Amazon Bedrock catalog\" → vendor=\"Amazon Bedrock\""
      },
      {
        "name": "get_market_stats",
        "description": "Aggregate AI inference market intelligence.\n\nReturns total vendor/model/SKU counts, price distribution (median, mean, quartiles, min/max), and modality breakdown. Optionally filter by modality.\n\nExamples:\n  - \"AI inference market overview\" → (no params)\n  - \"Text model pricing statistics\" → modality=\"Text\"\n  - \"Image generation market stats\" → modality=\"Image\""
      },
      {
        "name": "get_index_benchmarks",
        "description": "AIPI (ATOM Inference Price Index) — chained matched-model price benchmarks for AI inference.\n\nReturns benchmark indexes across four categories:\n- Modality: Text, Multimodal, Image, Audio, Video, Voice, Embeddings - what does this type of inference cost?\n- Channel: Model Developers, Cloud Marketplaces, Inference Platforms, Neoclouds - where should you buy?\n- Tier: Frontier, Budget, Mid, Reasoning - what's the premium for capability?\n- Special: Open-Source - how much cheaper is open-weight inference?\n\nEach index includes input, cached input, and output pricing per period.\n\nThese are market-wide benchmarks, not individual vendor prices. Use them to understand where the market is and how it's moving.\n\nFully public — available to all tiers.\n\nExamples:\n  - \"What's the current benchmark for text inference?\" → index_category=\"Modality\"\n  - \"Show me all AIPI indexes\" → (no params)\n  - \"Neocloud pricing benchmark\" → index_code=\"AIPI NCL GLB\"\n  - \"Channel pricing comparison\" → index_category=\"Channel\"\n  - \"Open-source vs market pricing\" → index_code=\"AIPI OSS GLB\""
      },
      {
        "name": "get_kpis",
        "description": "ATOM Inference Market KPIs — 9 cost and structure metrics derived from live pricing data across all tracked vendors:\n- Output Price Premium: how much more output tokens cost vs input\n- Caching Discount Rate: average discount for cached input pricing\n- Open Source Discount Rate: price gap between open-source and proprietary\n- Context Window Cost: price multiplier for 128K+ vs smaller context\n- Model Size Spread: price ratio between large and small models\n- Reasoning Premium: cost of reasoning models vs standard text\n- Platform Discount Rate: inference platforms vs buying direct\n- Neocloud Discount Rate: GPU-native providers vs model developers\n- Caching Availability: % of text models offering cached pricing\n\nThese KPIs are available to all tiers — they demonstrate ATOM's market intelligence."
      },
      {
        "name": "get_model_intelligence",
        "description": "ATOM Model Intelligence — 6 capability and coverage metrics derived from the metadata behind every tracked model. Complements the pricing KPIs in get_kpis.\n\nReturns 6 metrics:\n- Reasoning Tier Share: % of general-purpose text models that are reasoning-tier\n- Long-Context Saturation: % of models shipping 128K+ context windows\n- Frontier Context Ceiling: context multiplier between top-decile and median models\n- Output Ceiling Spread: max output token multiplier between top-decile and median\n- Training Cutoff Lag: median months between model training cutoff and today\n- Vendor Modality Breadth: median number of modalities offered per vendor\n\nRead alongside pricing, these explain why a model is priced the way it is. Available to all tiers.\n\nExamples:\n  - \"How stale are AI models on average?\" → Training Cutoff Lag\n  - \"What share of models support long context?\" → Long-Context Saturation\n  - \"How rare are reasoning models?\" → Reasoning Tier Share"
      },
      {
        "name": "list_vendors",
        "description": "List all AI inference vendors tracked by ATOM.\n\nReturns vendor name, country, region, and pricing page URL. Vendors span four channel types: Model Developers, Cloud Marketplaces, Inference Platforms, and Neoclouds. Optionally filter by region or country.\n\nExamples:\n  - \"List all vendors\" → (no params)\n  - \"European AI vendors\" → region=\"Europe\"\n  - \"Chinese AI vendors\" → country=\"China\""
      }
    ],
    "profiled_at": "2026-08-13T04:01:42.485Z"
  }
}