mcp server
ATOM Pricing Intelligence
The Global Price Benchmark for AI Inference. 1,600+ SKUs, 40+ vendors, 14 price indexes.
Description as published by the maintainer. Source
- version 1.1.2
- active
- evaluation
active — Most recent push to the repository was 2026-06-23. Dashed tags are derived by ZBS Index from the published description, not stated by the maintainer.
What this server can do
9 functions, named and described by the server itself. Parameter names are shown because they say more about what a function does than its name usually does.
compare_prices(limit, modality, direction, model_name, model_family, _atom_api_key)- 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"
get_index_benchmarks(limit, index_code, _atom_api_key, index_category)- 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"
get_kpis(_atom_api_key)- 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.
get_market_stats(modality, _atom_api_key)- 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"
get_model_detail(model_name, _atom_api_key)- 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" Required: model_name.
get_model_intelligence(_atom_api_key)- 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
get_vendor_catalog(limit, vendor, modality, direction, _atom_api_key)- 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" Required: vendor.
list_vendors(region, country, _atom_api_key)- 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"
search_models(limit, offset, vendor, creator, modality, direction, max_price, open_source, model_family, _atom_api_key, min_context_window, min_parameter_count)- 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
Last successful function declaration observed on . Source: https://atom-mcp-server-production.up.railway.app/mcp. We list what the server declared; we do not call any of these functions.
Endpoint status observed on . Source: https://atom-mcp-server-production.up.railway.app/mcp.
Signals
These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.
| Signal | Value | What it measures | Window | Observed | Source |
|---|---|---|---|---|---|
| GitHub stars | 0 | Number of GitHub accounts that bookmarked this repository since it was created. It is a bookmark count, not installs, not active users and not quality. | cumulative, all time | GitHub | |
| Last commit | 2026-06-23 | Date of the most recent push to any branch. This is the strongest cheap indicator of whether the project is still maintained. | point in time | GitHub | |
| Open issues | 0 | Open issues plus open pull requests, as GitHub counts them together. A high number can mean an active project or an abandoned one. | as of fetch | GitHub | |
| Latest published version | 1.1.2 | Latest version string the maintainer published to the registry. | as of fetch | Model Context Protocol | |
| Registry record last updated | 2026-03-07 | When the registry record was last updated by its maintainer. | point in time | Model Context Protocol | |
| First listed in the MCP Registry | 2026-03-07 | Date this server was first published to the official MCP Registry. Not a usage or quality measure. | point in time | Model Context Protocol | |
| repository status | active | The repository exists on GitHub and is not archived. This says nothing about how recently it was worked on. | as of fetch | GitHub | |
| mcp tools declared | 9 tools | Number of functions the server itself declared when asked to list them. This is what the server offers an agent, not a measure of how well any of them work. | as of probe | atom-mcp-server-production.up.railway.app | |
| mcp endpoint status | ok | The server listed 9 functions when asked. | as of probe | atom-mcp-server-production.up.railway.app |
Where to get it
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These share tags the maintainers applied themselves, such as llm-pricing, market-data. Common tags like "mcp" or "ai" are ignored for this: agreeing with six hundred other projects is not a similarity.
This is not a recommendation and not a test result. It is a map of what the authors said their work is about.
How the author describes it
Topics the maintainer set on GitHub: ai-inference, ai-pricing, aipi, llm, llm-pricing, market-data, mcp, mcp-server, pricing-intelligence.
This record as data
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