OptimToken MCP server

• compare-compute-pricingFree

  Compare cloud compute instance pricing across AWS, Azure, GCP, DigitalOcean, OCI, OVH, and Alibaba. Filter by region, provider, vCPUs, memory, category, processor, or use case. All prices are Linux on-demand list prices in USD. Not every price column is live: `provenance.priceTypes` says which of each provider's price columns come from a live API, which are static constants, and which are unavailable, and `provenance.staticPriceColumns` lists the non-live ones outright; a savings-plan or reserved rate that appears there is a static estimate, not a live quote. Prices are returned as the catalogue serves them and dated by `provenance`.

• compare-llm-modelsFree

  Browse and filter the whole LLM catalogue and get back a ranked table: price, quality (ELO), efficiency and capabilities. Use this when the user wants to SEE THE FIELD — 'show me models under $1/1M', 'which providers have vision models', 'list open-weight models above ELO 1300'. For a single PICK under a budget use recommend-llm-model; to weigh 2-4 NAMED models against each other use compare-models-side-by-side. Prices come from optimtoken.optimnow.io where reachable; the response's `provenance` says which tier served them and whether they are vendor-verified. Filter by provider, price tier (category), openness, capability, price range, or minimum ELO score. Optionally enrich with business metrics for a use case. Price tier and openness are independent: a model can be Frontier-priced and open-weight at once. Reports both list-price cost and the optimized cost achievable with prompt caching and the batch API. Prices, costs and scores are final as returned: rounded and ranked server-side, and dated by `provenance`. The text block is a numbered table of the same figures.

• compare-models-side-by-sideFree

  Compare 2-4 named LLM models against all 8 use-case profiles at a chosen monthly volume, showing list and optimized cost for each. Use when the user names specific models to weigh against each other, rather than filtering the whole catalogue. If they also supply their own token counts, or a volume outside 10k/100k/1m, use estimate-llm-cost instead. Every name is resolved against the catalogue and the outcome is reported in `resolution` and in the text: a name that matched nothing, matched several models, or duplicated an earlier pick is stated explicitly, and such a name gets no column rather than a zero cost. Prices and costs are final as returned: rounded server-side and dated by `provenance`.

• estimate-llm-costFree

  Cost a workload with EXACT numbers the caller supplies: arbitrary token counts per request and any monthly volume, not just the 10k/100k/1m presets the other cost tools use. Use this for 'about 800 in and 200 out, 4 million calls a month', or to price one named model across every use-case profile. To compare 2-4 named models like for like at a preset volume, use compare-models-side-by-side instead. Provide a model name to get detailed cost breakdowns, or compare costs across all use case presets. Each figure comes twice: list price, and the optimized price achievable with prompt caching and the batch API. Cost figures are final as returned: rounded server-side and dated by `provenance`, with the lever behind each saving, or the reason there is none, named in the text.

• recommend-llm-modelFree

  Pick a model. Returns a ranked top 3 for one workload under optional constraints, each with a per-constraint satisfied/violated breakdown as the evidence. Use this when the user wants an ANSWER rather than a table — 'what should I use for support tickets under $500 a month'. To browse or filter the whole catalogue instead, use compare-llm-models. Constraints: (monthly budget, minimum ELO, required capability, self-hostability). Returns a top 3 as structured facts — efficiency rank, ELO, list and optimized cost, FinOps flag, volatility, and a per-constraint satisfied/violated breakdown. When nothing satisfies every constraint the query is reported as over-constrained, `recommendations` is empty, and the nearest misses are returned in `nearMisses` instead, each carrying the constraint it failed and flagged as a miss in the text. The ranking is computed server-side and is final.

• –Code scanremote-only server, no package to scann/a
• Live reliabilityremote reachable in 6662ms12/20
• Tool poisoning5 tool descriptions checked15/15
• Auth qualityopen endpoint, read-only tools10/15
• Maintenancelast push 4 days ago15/15
• Maintainer identityregistry namespace matches repository owner; GitHub account older than a year8/10

Safety score

80/100

higher than 60% of 4,460 AI and agents servers

Tools

5

the typical AI and agents server has 6

Tools that change things

0%

typical: 0%. Fewer write tools means less an agent can break

Uptime

100%

of our recent checks; the typical remote AI and agents server answers 100%

Response time

206 ms

faster than 64% of 2,331 AI and agents servers

Grade history

• 2026-11-03restoreD → Bscore 80

OptimToken: common questions

Is OptimToken MCP server safe?

Mostly: it is graded B (80/100). Read the OptimToken safety report

How do I install OptimToken?

It runs remotely at optimtoken-mcp.optimnow.io. Add it to Claude Code, Cursor, VS Code or Claude Desktop with the snippets above, or call it through the mcp.market gateway without installing anything.

Does OptimToken need an API key?

Not as far as the registry entry and our scan can tell: no credentials are declared or required.

Is OptimToken maintained?

The last commit was 5 days ago (2026-10-30). The latest release is v0.4.2.

Is OptimToken up?

100% of our last 28 checks got an answer. We check remote servers about four times a day.

What can I use instead of OptimToken?

Servers from other publishers that do the same job: FireCMS Cloud MCP server, Actual Budget MCP Server and Alibaba Cloud DashScope — Qwen MCP server.

How does OptimToken compare with other AI and agents MCP servers?

safety score 80/100 (higher than 60% of 4,460 AI and agents servers); tools 5 (the typical AI and agents server has 6); tools that change things 0% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.