Atom Mcp Server — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Atom Mcp Server (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
Aggregate score unchanged between these scans.
The primary manifest — the file an agent reads to learn what this artifact does.
<p align="center"> <img src="https://raw.githubusercontent.com/StamatiosKanellakis/A7OM/main/ATOM_Logo_Gray.png" alt="ATOM" width="200" /> </p>
<h1 align="center">ATOM MCP Server</h1>
<p align="center"> <strong>The Global Price Benchmark for AI Inference, delivered as a native tool for AI agents.</strong><br/> Independent pricing intelligence for developers, analysts, and infrastructure buyers. Transparent methodology, deterministic indexing, weekly market intelligence across the global AI inference market. </p>
<p align="center"> <a href="https://a7om.com">Website</a> · <a href="https://a7om.com/about">About ATOM</a> · <a href="https://a7om.com/methodology">Methodology</a> · <a href="https://a7om.com/mcp">ATOM MCP Pro</a> </p>
ATOM MCP Server lets any MCP-compatible AI agent (Claude, GPT, Cursor, Windsurf, VS Code Copilot) query live AI inference pricing data programmatically. Built on financial index methodology comparable to S&P Dow Jones, MSCI, and Bloomberg, the ATOM Inference Price Index (AIPI) is the first independent benchmark for the AI inference market.
Ask your AI assistant a question like "What's the cheapest way to run GPT-4o?" and it calls ATOM's tools behind the scenes, returning a data-backed answer pulled from live coverage across the global vendor landscape.
The AIPI index family spans four categories, all calculated weekly using chained matched-model methodology to eliminate composition bias:
| Category | Examples | What It Answers |
|---|---|---|
| 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: direct, marketplace, platform, or neocloud? |
| Tier | Frontier, Budget, Mid-Tier, Reasoning | What is the premium for capability? |
| Special | Open-Source | How much cheaper is open-weight inference? |
All indexes are global (GLB) and reported across three pricing directions: input, cached input, output.
Two complementary intelligence layers. Pricing tells you what inference costs; Model Intelligence tells you what you are paying for.
| Tool | Tier | Description |
|---|---|---|
list_vendors | Free | All tracked vendors with country, region, channel type, and pricing page URLs |
get_kpis | Free | 9 pricing KPIs: output premium, caching discount, open-source discount, context window cost, model size spread, reasoning premium, platform discount, neocloud discount, caching availability |
get_model_intelligence | Free | 6 capability KPIs from model metadata: reasoning tier share, long-context saturation, frontier context ceiling, output ceiling spread, training cutoff lag, vendor modality breadth |
get_index_benchmarks | Free | AIPI price benchmarks across all indexes |
get_market_stats | Tiered | Aggregate market intelligence: medians, quartiles, distributions, modality breakdown |
search_models | Tiered | Multi-filter search: modality, vendor, creator, open-source, price range, context window, parameters |
get_model_detail | Tiered | Full specs and pricing across all vendors for a single model |
compare_prices | Tiered | Cross-vendor price comparison for a model or model family |
get_vendor_catalog | Tiered | Complete catalog for a specific vendor |
| ATOM MCP (Free) | ATOM MCP Pro | |
|---|---|---|
| Vendors, KPIs, Model Intelligence, AIPI indexes | Full data | Full data |
| Market stats | Aggregates only | Vendor-level breakdown |
| Model search and comparison | Counts and price ranges | Full granular SKU data |
| Model detail | Specs only | Per-vendor pricing |
| Vendor catalog | Summary only | Full SKU listing |
Free tier (no API key): enough to understand the market through counts, ranges, distributions, benchmarks, and capability metrics.
ATOM MCP Pro: full granular data across every vendor, model, price, and spec. Subscribe at a7om.com/mcp.
No install required. Connect directly to ATOM's hosted server.
Claude.ai (web): Settings → Connectors → Add custom connector
Name: ATOM Pricing Intelligence
URL: https://atom-mcp-server-production.up.railway.app/mcpClaude Desktop: Settings → Developer → Edit Config
{
"mcpServers": {
"atom-pricing": {
"url": "https://atom-mcp-server-production.up.railway.app/mcp"
}
}
}Note: Remote URL support requires a recent Claude Desktop version. If it does not work, use the npx method below.
Claude Desktop (via npx proxy):
{
"mcpServers": {
"atom-pricing": {
"command": "npx",
"args": ["mcp-remote", "https://atom-mcp-server-production.up.railway.app/mcp"]
}
}
}git clone https://github.com/A7OM-AI/atom-mcp-server.git
cd atom-mcp-server
npm install && npm run buildAdd to your MCP client config:
{
"mcpServers": {
"atom-pricing": {
"command": "node",
"args": ["/path/to/atom-mcp-server/dist/index.js"],
"env": {
"SUPABASE_URL": "https://jonncmzxvxzwyaznokba.supabase.co",
"SUPABASE_ANON_KEY": "your-anon-key"
}
}
}
}Set environment variables in Railway dashboard:
SUPABASE_URLSUPABASE_ANON_KEYATOM_API_KEYS (comma-separated, for paid tier validation)TRANSPORT=httpOnce connected, ask your AI assistant in natural language:
| Variable | Required | Description |
|---|---|---|
SUPABASE_URL | Yes | Supabase project URL |
SUPABASE_ANON_KEY | Yes | Supabase anonymous/public key |
ATOM_API_KEYS | No | Comma-separated valid API keys for paid tier |
TRANSPORT | No | stdio (default) or http |
PORT | No | HTTP port (default 3000) |
@modelcontextprotocol/sdk)The Global Price Benchmark for AI Inference. Independent pricing intelligence for developers, analysts, and infrastructure buyers. Transparent methodology, deterministic indexing, weekly market intelligence across the global AI inference market.
ATOM was founded in 2025 by Stamos Kanellakis. The platform is built on financial index methodology comparable to S&P Dow Jones, MSCI, and Bloomberg, and applies that discipline to a market that previously had no pricing benchmark of its own.
Products: ATOM MCP · ATOM Terminal · ATOM Feed
MIT
<p align="center"><strong>ATOM</strong> · <em>The Global Price Benchmark for AI Inference.</em></p>
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.