# ATOM Pricing Intelligence MCP server

The Global Price Benchmark for AI Inference. 1,600+ SKUs, 40+ vendors, 14 price indexes.

## Links
- Registry page: https://www.getdrio.com/mcp/io-github-a7om-ai-atom-mcp-server
- Repository: https://github.com/A7OM-AI/atom-mcp-server
- Website: https://a7om.com/mcp

## Install
- Command: `npx -y atom-mcp-server`
- Endpoint: https://atom-mcp-server-production.up.railway.app/mcp
- Auth: Not captured

## Setup notes
- Package: Npm atom-mcp-server v1.1.2
- Remote endpoint: https://atom-mcp-server-production.up.railway.app/mcp

## Tools
- search_models (Search AI Models) - Search and filter AI inference models across all tracked vendors and SKUs.

Query by modality (Text, Image, Audio, Video, Multimodal), vendor, creator, model family, open-source status, price range, context window, and parameter count.

Returns matching models with pricing. Free tier shows count + price range; paid tier shows full details.

Examples:
  - "Find open-source text models under $1/M tokens" → open_source=true, modality="Text", max_price=0.001
  - "What multimodal models does Google offer?" → vendor="Google", modality="Multimodal"
  - "Models with 128K+ context window" → min_context_window=128000 Endpoint: https://atom-mcp-server-production.up.railway.app/mcp
- get_model_detail (Get Model Details) - Deep dive on a single AI model: technical specs + pricing across all vendors.

Returns model_registry data (context window, parameters, open-source status, training cutoff, model family) plus all SKU pricing across every vendor that offers this model.

Examples:
  - "Tell me everything about GPT-4o" → model_name="GPT-4o"
  - "Claude Sonnet 4.5 specs and pricing" → model_name="Claude Sonnet 4.5" Endpoint: https://atom-mcp-server-production.up.railway.app/mcp
- compare_prices (Compare Prices Across Vendors) - Cross-vendor price comparison for a specific model or model family.

Shows the same model (or family) priced across different vendors, sorted cheapest first. Essential for cost optimization and vendor selection.

Examples:
  - "Compare Llama 3.1 70B pricing across vendors" → model_name="Llama 3.1 70B"
  - "Cheapest GPT-4 family output pricing" → model_family="GPT-4", direction="Output"
  - "Claude pricing comparison" → model_family="Claude" Endpoint: https://atom-mcp-server-production.up.railway.app/mcp
- get_vendor_catalog (Get Vendor Catalog) - Full catalog for a specific vendor: all models, modalities, and pricing.

Returns vendor metadata (country, region, pricing page URL) plus every model and SKU they offer.

Examples:
  - "What does Together AI sell?" → vendor="Together AI"
  - "OpenAI's text model pricing" → vendor="OpenAI", modality="Text"
  - "Amazon Bedrock catalog" → vendor="Amazon Bedrock" Endpoint: https://atom-mcp-server-production.up.railway.app/mcp
- get_market_stats (Get Market Statistics) - Aggregate AI inference market intelligence.

Returns total vendor/model/SKU counts, price distribution (median, mean, quartiles, min/max), and modality breakdown. Optionally filter by modality.

Examples:
  - "AI inference market overview" → (no params)
  - "Text model pricing statistics" → modality="Text"
  - "Image generation market stats" → modality="Image" Endpoint: https://atom-mcp-server-production.up.railway.app/mcp
- get_index_benchmarks (Get AIPI Index Benchmarks) - AIPI (ATOM Inference Price Index) — chained matched-model price benchmarks for AI inference.

Returns benchmark indexes across four categories:
- Modality: Text, Multimodal, Image, Audio, Video, Voice, Embeddings - what does this type of inference cost?
- Channel: Model Developers, Cloud Marketplaces, Inference Platforms, Neoclouds - where should you buy?
- Tier: Frontier, Budget, Mid, Reasoning - what's the premium for capability?
- Special: Open-Source - how much cheaper is open-weight inference?

Each index includes input, cached input, and output pricing per period.

These are market-wide benchmarks, not individual vendor prices. Use them to understand where the market is and how it's moving.

Fully public — available to all tiers.

Examples:
  - "What's the current benchmark for text inference?" → index_category="Modality"
  - "Show me all AIPI indexes" → (no params)
  - "Neocloud pricing benchmark" → index_code="AIPI NCL GLB"
  - "Channel pricing comparison" → index_category="Channel"
  - "Open-source vs market pricing" → index_code="AIPI OSS GLB" Endpoint: https://atom-mcp-server-production.up.railway.app/mcp
- get_kpis (Get Market KPIs) - ATOM Inference Market KPIs — 9 cost and structure metrics derived from live pricing data across all tracked vendors:
- Output Price Premium: how much more output tokens cost vs input
- Caching Discount Rate: average discount for cached input pricing
- Open Source Discount Rate: price gap between open-source and proprietary
- Context Window Cost: price multiplier for 128K+ vs smaller context
- Model Size Spread: price ratio between large and small models
- Reasoning Premium: cost of reasoning models vs standard text
- Platform Discount Rate: inference platforms vs buying direct
- Neocloud Discount Rate: GPU-native providers vs model developers
- Caching Availability: % of text models offering cached pricing

These KPIs are available to all tiers — they demonstrate ATOM's market intelligence. Endpoint: https://atom-mcp-server-production.up.railway.app/mcp
- get_model_intelligence (Get Model Intelligence KPIs) - ATOM Model Intelligence — 6 capability and coverage metrics derived from the metadata behind every tracked model. Complements the pricing KPIs in get_kpis.

Returns 6 metrics:
- Reasoning Tier Share: % of general-purpose text models that are reasoning-tier
- Long-Context Saturation: % of models shipping 128K+ context windows
- Frontier Context Ceiling: context multiplier between top-decile and median models
- Output Ceiling Spread: max output token multiplier between top-decile and median
- Training Cutoff Lag: median months between model training cutoff and today
- Vendor Modality Breadth: median number of modalities offered per vendor

Read alongside pricing, these explain why a model is priced the way it is. Available to all tiers.

Examples:
  - "How stale are AI models on average?" → Training Cutoff Lag
  - "What share of models support long context?" → Long-Context Saturation
  - "How rare are reasoning models?" → Reasoning Tier Share Endpoint: https://atom-mcp-server-production.up.railway.app/mcp
- list_vendors (List All Vendors) - List all AI inference vendors tracked by ATOM.

Returns 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.

Examples:
  - "List all vendors" → (no params)
  - "European AI vendors" → region="Europe"
  - "Chinese AI vendors" → country="China" Endpoint: https://atom-mcp-server-production.up.railway.app/mcp

## Resources
Not captured

## Prompts
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## Metadata
- Owner: io.github.A7OM-AI
- Version: 1.1.2
- Runtime: Npm
- Transports: HTTP, STDIO
- License: Not captured
- Language: Not captured
- Stars: Not captured
- Updated: Mar 7, 2026
- Source: https://registry.modelcontextprotocol.io