OptimToken MCP

Built by OptimNow. Ask an AI assistant what a model or an instance actually costs, and get a dated, sourced figure instead of a number the model remembers from its training data.

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Connect in 30 seconds

The server is hosted, so there is nothing to install.

https://optimtoken-mcp.optimnow.io/mcp

Paste it exactly as written, with /mcp and no trailing slash. The widgets render only for that form: the host checks a hash of the URL you entered.

Client	How to add it
	claude mcp add --transport http optimtoken https://optimtoken-mcp.optimnow.io/mcp
	Settings → Connectors → Add custom connector, paste the URL above
	Settings → Connectors → Add, paste the URL. Comparisons render as interactive widgets
	Add an HTTP MCP server entry pointing at the URL

Then just ask:

"We send 200k support tickets a month at about 1,500 input tokens each. Which model gives me the best quality per euro, and what would it cost?"

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Why this exists

Model prices change weekly, and a language model's idea of them is frozen at its training cutoff. Ask one what Claude or GPT costs and you get a confident answer that was true some months ago, with no date attached and no way to tell. The same applies to cloud instance rates, which additionally vary by region in ways nobody memorises.

This server replaces recall with a lookup:

• Live prices, not remembered ones. Every LLM figure comes from the OptimToken catalogue, which tracks 250+ models and refreshes daily.
• Corrected prices. OptimToken keeps a committed price archive, a verified overrides table checked against vendor pricing pages, and an anomaly check that alarms on the half-price and double-price breaks upstream feeds occasionally publish. This server asks that catalogue rather than re-deriving prices itself.
• Cost per request, not cost per million tokens. Price-per-token comparisons hide the thing you actually pay for. The tools apply your token shape, your cache hit rate and batch eligibility, and return a figure per request and per month.
• Every answer carries its provenance. Which tier served it, and whether the prices were verified.

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Tools

Tool	What it answers
compare-llm-models	"What is out there?" Browse and filter the catalogue on price, quality (Chatbot Arena ELO), efficiency and capabilities, with a self-hostability read from the licence.
recommend-llm-model	"Just tell me which one." A ranked top 3 for one workload under your constraints (budget, minimum ELO, required capability, self-hostability), each with a per-constraint satisfied or violated breakdown as the evidence. Over-constrained queries return the nearest misses, labelled as such.
compare-models-side-by-side	"How do these specific ones compare?" 2 to 4 named models across all 8 use case profiles at a chosen monthly volume, list and optimized cost for each.
estimate-llm-cost	"What will this cost us per month?" Per-request and monthly cost for your own volume, token shape, cache hit rate and batch eligibility.
compare-compute-pricing	"What should we run it on?" Compute instance rates across AWS, Azure, GCP, OCI, OVH, DigitalOcean and Alibaba, by region and category.

All five are read-only and take no credentials. Nothing you send is stored.

Use case profiles ship with realistic token shapes, so you do not have to invent them: Support Ticket, Knowledge Q&A, Meeting Summary, Marketing Content, Coding Task, Invoice Processing, Call Summary, Agent Workflow.

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Where the numbers come from

optimtoken.optimnow.io is the single source of truth. When it cannot be reached, the server degrades in tiers rather than failing, and says which tier it used.

Tool	Tier 1	Tier 2	Tier 3
LLM tools	GET /api/llm-models	OpenRouter direct	embedded snapshot
Compute tool	GET /api/pricing?region=	not available	embedded snapshot (137 rows)

Tiers 2 and 3 serve uncorrected prices, and that matters more than it sounds. An upstream feed once published a frontier model at half its real list price, which halves every monthly figure derived from it. So every response carries a provenance object with pricesVerified, and the lower tiers put a notice at the top of the answer. A fallback should never quietly downgrade correctness.

Tier 1 is accepted only when the catalogue reports that it is itself serving fresh upstream data. If the site is on its own fallback, it carries no corrections, and this server treats it accordingly.

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Local development

Requires Node.js 24+.

npm install
npm run dev              # Skybridge dev server + MCP inspector at localhost:3000
npm test                 # schema conformance, serialisation precision, data sources
npm run build            # widgets + server

The static fallback catalogue is refreshed by hand, not on a schedule:

npm run refresh-fallback

Because it is manual, check its dataAsOf before trusting a tier-3 response. An unrefreshed fallback ages silently.

ai-pricing-hub-mcp/
├─ server/src/index.ts              # tool + widget registrations
├─ server/src/lib/optimtoken-api.ts # the one base URL constant, fetch and timeout discipline
├─ server/src/lib/                  # ranking, efficiency scoring, provenance, normalisation
├─ server/src/data/                 # static fallback pricing + region maps
└─ web/src/widgets/                 # React widgets rendered in the client

Built with Skybridge, deployed on Fly.io.

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The rest of the family

OptimToken	The web app. Same catalogue, full UI, an AI advisor and a public JSON API.
AI ROI Calculator	Does the AI business case pay for itself. Same prices, plus harness costs and value modelling.
cloud-finops-skills	FinOps knowledge for AI agents: AWS, Azure, GCP, AI inference, SaaS.
finops-mcp-resources	MCP servers, tutorials and client guides for cloud cost work.

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License

Released under the MIT License.

Prices served by this server come from third-party sources and are provided as is, without warranty. Verify against vendor pricing pages before committing spend.

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Questions about your own AI or cloud bill? Talk to OptimNow.