mcpbeat

ATOM Pricing Intelligence MCP Server

io.github.A7OM-AI/atom-mcp-server
answering

ATOM Pricing Intelligence is answering right now. Last checked 10 min ago. 64 installs a week from npm. It exposes 9 tools. Last commit 23 Jun 2026.

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

Installs per day peak 29 · avg 8 · +8% w/w
a month agotoday
Uptime history 43 hours of history
43 hours agonow
100.0%
Uptime 24h
91 of 91 checks
9
Tools
read from the server
353 ms
Response time
average over 24h
64
Installs / week
npm and PyPI

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 10 min ago.

run in your terminal
claude mcp add atom-mcp-server --transport http https://atom-mcp-server-production.up.railway.app/mcp
~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "atom-mcp-server": {
      "url": "https://atom-mcp-server-production.up.railway.app/mcp"
    }
  }
}
~/.codex/config.toml
[mcp_servers.atom-mcp-server]
url = "https://atom-mcp-server-production.up.railway.app/mcp"
.cursor/mcp.json
{
  "mcpServers": {
    "atom-mcp-server": {
      "url": "https://atom-mcp-server-production.up.railway.app/mcp"
    }
  }
}
.vscode/mcp.json
{
  "mcpServers": {
    "atom-mcp-server": {
      "url": "https://atom-mcp-server-production.up.railway.app/mcp"
    }
  }
}

Available tools 9

Read directly from the server with tools/list, grouped by what they act on. If a tool disappears, we record the date.

model
get_model_detail
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"
get_model_intelligence
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
compare
compare_prices
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"
index
get_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"
kpis
get_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.
market
get_market_stats
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"
models
search_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
vendor
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"
vendors
list_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"

Endpoints

URLTransportStateLatencyChecked
https://atom-mcp-server-production.up.railway.app/mcp sse answering 349 ms 10 min ago

ATOM Pricing Intelligence — questions

Answers built from our own checks of this server.

What can ATOM Pricing Intelligence do?
It exposes 9 tools, read directly from the server on our last check. Among them: compare_prices, get_index_benchmarks, get_kpis, get_market_stats, get_model_detail, get_model_intelligence and 3 more. The full list with descriptions is on this page — we take it from the server itself via tools/list, not from a README. How MCP servers expose tools in the first place →
Is ATOM Pricing Intelligence working right now?
We send a real MCP handshake every 15 minutes. Over the last 24 hours 91 of 91 checks got a reply (100.0%), average response time 353 ms. The bar chart above shows every period we have measured.
How do I connect ATOM Pricing Intelligence?
Copy the ready config from this page — we generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each with the file path that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address.
Does ATOM Pricing Intelligence need an API key?
No. ATOM Pricing Intelligence completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 9 of them are readable on this page. This is what we observed, not what the docs claim.
How fast is ATOM Pricing Intelligence?
It answers our handshake in 353 ms on average, which is faster than 40% of all working MCP servers we measure. The comparison comes from our own checks across the whole registry, every 15 minutes.
How many people use ATOM Pricing Intelligence?
The npm package atom-mcp-server was installed 64 times in the last week. Week over week that is +8%. We show installs rather than GitHub stars on purpose: a star is a bookmark, an install is someone actually running it.
Is ATOM Pricing Intelligence open source?
Yes — written in TypeScript and 0 stars on GitHub. The source link is on this page, so you can read exactly what it does with your data before you connect it.