# OptimToken (remote · optimtoken-mcp.optimnow.io)

Compare LLM API and cloud compute pricing, estimate workload costs, pick a model within budget.

- Trust score: 84/100 (high trust)
- Change this week: +4
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-11-04

## Components

- remote · `optimtoken-mcp.optimnow.io`: 84/100 (this document), [markdown](https://verifymcp.io/servers/optimnow-ai-pricing-hub/optimtoken-mcp.md), [page](https://verifymcp.io/servers/optimnow-ai-pricing-hub/optimtoken-mcp)

## Channel facts

- Endpoint: `https://optimtoken-mcp.optimnow.io/mcp`
- Transports: `streamable-http`
- Auth: `none`
- Version: `0.4.2`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-11-04.

- **Endpoint Security**: 74/100
  - The endpoint's TLS certificate is valid, in date, and uses a strong key.
  - No authorisation is required to call this server. Every tool declares its destructiveHint and none is destructive, so open access doesn't expose one.
  - HTTPS is enforced; there's no plaintext access path.
  - HSTS check failed: the Strict-Transport-Security header is absent.
  - DNSSEC check failed: this domain isn't protected by DNSSEC.
- **Transport & Reachability**: 100/100
  - Verified streamable-http transport via a live MCP handshake.
- **Schema Quality & AI Usability**: 74/100
  - 100% of prompts and resources have a non-trivial description (not blank, and not just the item's name).
  - AI-judged instruction clarity (good).
  - Context-footprint check failed: tool/resource definitions use about 2459 tokens (~245/item across 10 items; 5 tools + 5 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 90/100
  - Stability observed for 27 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 100/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 100% of tool parameters carry a description.
  - Structured output schemas are declared (100% of tools); any adoption earns full credit.
- **Tool Safety**: 100/100
  - No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.
  - We read all 5 captured tool definition(s), and no name or description among them implies an irreversible operation.
  - An AI judge read all 6 captured unit(s) of tool text and found none that tries to manipulate the model reading it.
- **Capabilities**: 100/100
  - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.
  - Supports UI / widget rendering.

## Install

### How do I install the OptimToken MCP server?

OptimToken is a hosted endpoint at https://optimtoken-mcp.optimnow.io/mcp, so there is nothing to install locally. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.

### Claude

```bash
claude mcp add --transport http optimnow-ai-pricing-hub 'https://optimtoken-mcp.optimnow.io/mcp'
```

### Cursor

```json
{
  "mcpServers": {
    "optimnow-ai-pricing-hub": {
      "url": "https://optimtoken-mcp.optimnow.io/mcp"
    }
  }
}
```

### VS Code

```json
{
  "servers": {
    "optimnow-ai-pricing-hub": {
      "type": "http",
      "url": "https://optimtoken-mcp.optimnow.io/mcp"
    }
  }
}
```

### Codex

```toml
[mcp_servers.optimnow-ai-pricing-hub]
url = "https://optimtoken-mcp.optimnow.io/mcp"
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "optimnow-ai-pricing-hub": {
      "type": "remote",
      "url": "https://optimtoken-mcp.optimnow.io/mcp",
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add optimnow-ai-pricing-hub --url 'https://optimtoken-mcp.optimnow.io/mcp' --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  optimnow-ai-pricing-hub:
    url: "https://optimtoken-mcp.optimnow.io/mcp"
```

### Netclaw

```json
{
  "McpServers": {
    "optimnow-ai-pricing-hub": {
      "Transport": "http",
      "Url": "https://optimtoken-mcp.optimnow.io/mcp"
    }
  }
}
```

### Vellum

```bash
assistant mcp add optimnow-ai-pricing-hub -t streamable-http -u 'https://optimtoken-mcp.optimnow.io/mcp'
```

### Other

```json
{
  "mcpServers": {
    "optimnow-ai-pricing-hub": {
      "type": "http",
      "url": "https://optimtoken-mcp.optimnow.io/mcp"
    }
  }
}
```

The mcpServers block is a cross-client convention. Remote transports vary, so check your client's docs.

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-11-04 (score 84, +1)

No change was recorded against any check on this day. Stability & Change Management went from 87 to 90. That category is still filling its 30-day observation window: 26 days of observed history at the previous scan, 27 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-11-02 (score 83, +1)

No change was recorded against any check on this day. Stability & Change Management went from 80 to 83. That category is still filling its 30-day observation window: 24 days of observed history at the previous scan, 25 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-31 (score 82, +1)

No change was recorded against any check on this day. Stability & Change Management went from 73 to 77. That category is still filling its 30-day observation window: 22 days of observed history at the previous scan, 23 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-29 (score 81, +1)

No change was recorded against any check on this day. Stability & Change Management went from 67 to 70. That category is still filling its 30-day observation window: 20 days of observed history at the previous scan, 21 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-27 (score 80, +1)

No change was recorded against any check on this day. Stability & Change Management went from 60 to 63. That category is still filling its 30-day observation window: 18 days of observed history at the previous scan, 19 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-25 (score 79, +1)

No change was recorded against any check on this day. Stability & Change Management went from 53 to 57. That category is still filling its 30-day observation window: 16 days of observed history at the previous scan, 17 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-22 (score 78, +1)

No change was recorded against any check on this day. Stability & Change Management went from 43 to 47. That category is still filling its 30-day observation window: 13 days of observed history at the previous scan, 14 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-20 (score 77, +1)

No change was recorded against any check on this day. Stability & Change Management went from 37 to 40. That category is still filling its 30-day observation window: 11 days of observed history at the previous scan, 12 at this one. The score rises as the window fills, whether or not the server changes.

## MCP tools (5)

### `compare-llm-models` (~654 tokens)

Compare LLM Models

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.

Input parameters:

- `capability` (string): Capability, matched by exact equality (case-insensitive): Text, Vision, Reasoning, Agents, Image Gen, Audio. Any other string returns zero matches. There is no Code capability: for what coding costs,…
- `category` (string): Price tier, matched by exact equality (case-insensitive): Frontier, Mid-tier, Budget, Image. This is cost only — self-hostability is the separate `openness` axis.
- `limit` (number): Max models to return (default: 15)
- `maxInputPrice` (number): Max input price per 1M tokens in USD
- `maxOutputPrice` (number): Max output price per 1M tokens in USD
- `minElo` (number): Minimum Chatbot Arena ELO score. Typical range 1000-1500; ~1400 is roughly frontier-class. Models with no ELO score never satisfy this.
- `openness` (string): Filter by self-hostability, derived from the licence: Open source, Open weights, Proprietary, Unknown
- `provider` (string): Filter by provider name (e.g. 'OpenAI', 'Anthropic', 'Google')
- `useCasePreset` (string): Workload shape, which sets tokens per request: supportTicket (1.5k in / 500 out), knowledgeQA (2k / 800), meetingSummary (10k / 1.2k, batch-eligible), marketingContent (2.5k / 1.8k), codingTask (3k /…
- `volumePreset` (string): Monthly request volume: 10k, 100k, or 1m. Default: 100k

Output parameters:

- `catalogSize` (number)
- `dataAsOf` (string)
- `eloAsOf` (string)
- `error` (string)
- `finopsBadge` (object)
- `matchingCount` (number)
- `models` (array)
- `provenance` (object)
- `source` (string)
- `useCaseLabel` (string)
- `volumeLabel` (string)

### `estimate-llm-cost` (~501 tokens)

Estimate LLM Cost

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.

Input parameters:

- `customInputTokens` (integer): Custom input tokens per request. Must be supplied TOGETHER with customOutputTokens — either alone is ignored and the preset is used. A custom shape assumes no cacheable prefix and no batch eligibilit…
- `customOutputTokens` (integer): Custom output tokens per request. Must be supplied TOGETHER with customInputTokens — either alone is ignored and the preset is used.
- `modelName` (string): Model name, e.g. 'GPT-4o'. A partial name matches up to 5 models and ALL of them are costed. If omitted, the first 8 catalogue entries are used — that is catalogue order, not a quality ranking.
- `monthlyVolume` (integer): Exact monthly request count, any integer (default 100,000). This tool does not take the 10k/100k/1m presets the other cost tools use.
- `useCasePreset` (string): Workload shape, which sets tokens per request: supportTicket (1.5k in / 500 out), knowledgeQA (2k / 800), meetingSummary (10k / 1.2k, batch-eligible), marketingContent (2.5k / 1.8k), codingTask (3k /…

Output parameters:

- `dataAsOf` (string)
- `eloAsOf` (string)
- `error` (string)
- `modelCosts` (array)
- `provenance` (object)
- `source` (string)
- `volume` (number)

### `compare-models-side-by-side` (~235 tokens)

Compare Models Side by Side

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`.

Input parameters:

- `models` (array, required): 2-4 model names to compare, e.g. ['GPT-4o', 'Claude Opus 5', 'Gemini 3.1 Pro']
- `volumePreset` (string): Monthly request volume: 10k, 100k, or 1m. Default: 100k

Output parameters:

- `dataAsOf` (string)
- `eloAsOf` (string)
- `error` (string)
- `models` (array)
- `provenance` (object)
- `resolution` (array)
- `source` (string)
- `volume` (number)
- `volumeLabel` (string)

### `recommend-llm-model` (~500 tokens)

Recommend an LLM Model

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.

Input parameters:

- `maxMonthlyBudget` (number): Maximum monthly budget in USD at the given volume. Tested against the LIST-price monthly cost, not the caching/batch-optimized cost.
- `minElo` (number): Minimum Chatbot Arena ELO score. Typical range 1000-1500; ~1400 is roughly frontier-class. Models with no ELO score never satisfy this.
- `openness` (string): Require a self-hostability bucket, derived from the licence: Open source, Open weights, Proprietary, Unknown
- `requiredCapability` (string): Capability the model must have: Text, Vision, Reasoning, Agents, Image Gen, Audio. There is no Code capability: for coding, pass useCasePreset codingTask.
- `useCasePreset` (string, required): Workload shape, which sets tokens per request: supportTicket (1.5k in / 500 out), knowledgeQA (2k / 800), meetingSummary (10k / 1.2k, batch-eligible), marketingContent (2.5k / 1.8k), codingTask (3k /…
- `volumePreset` (string): Monthly request volume: 10k, 100k, or 1m. Default: 100k

Output parameters:

- `candidateCount` (number)
- `catalogSize` (number)
- `dataAsOf` (string)
- `eloAsOf` (string)
- `error` (string)
- `nearMisses` (array)
- `overConstrained` (boolean)
- `provenance` (object)
- `rankedCount` (number)
- `recommendations` (array)
- `roiCalculatorUrl` (string)
- `source` (string)
- `useCaseLabel` (string)
- `volume` (number)
- `volumeLabel` (string)

### `compare-compute-pricing` (~500 tokens)

Compare Cloud Compute Pricing

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`.

Input parameters:

- `category` (string): Instance category. Must be one of these exactly (case-insensitive): General Purpose, Compute Optimized, Memory Optimized, Storage Optimized, GPU / Accelerated, Burstable. Any other string returns zer…
- `limit` (number): Max instances to return (default: 20)
- `maxMemory` (number): Maximum memory in GiB
- `maxVCPUs` (number): Maximum number of vCPUs
- `minMemory` (number): Minimum memory in GiB
- `minVCPUs` (number): Minimum number of vCPUs
- `processor` (string): Processor. Matched by exact equality (case-insensitive), so a partial value like 'H100' returns nothing. Known values: Intel, AMD, Graviton, Ampere, NVIDIA A100, NVIDIA H100, NVIDIA L4, NVIDIA T4, NV…
- `provider` (string): Cloud provider. Must be one of these exactly (case-insensitive): AWS, Azure, GCP, DigitalOcean, OCI, OVH, Alibaba. Any other string returns zero matches rather than an error.
- `region` (string): Pricing region: us-east, us-west, europe, asia-pacific. Default: us-east
- `sortBy` (string): Sort by: price, vcpus, memory, pricePerVCPU. Default: price
- `useCase` (string): Use case. Must be one of these exactly (case-insensitive): Web App, Database, HPC, ML & AI, Dev/Test, Big Data. Any other string returns zero matches rather than an error.

Output parameters:

- `catalogSize` (number)
- `error` (string)
- `instances` (array)
- `matchingCount` (number)
- `provenance` (object)
- `source` (string)

## Diagnostics

Captured diagnostic sections: TLS, DNSSEC, Authorisation, Transports. The full working is on the page: https://verifymcp.io/servers/optimnow-ai-pricing-hub/optimtoken-mcp#diagnostics

## Score history

- 2026-11-04: 84
- 2026-11-03: 83
- 2026-11-02: 83
- 2026-11-01: 82
- 2026-10-31: 82
- 2026-10-30: 81
- 2026-10-29: 81
- 2026-10-28: 80
- 2026-10-27: 80
- 2026-10-26: 79
- 2026-10-25: 79
- 2026-10-24: 78
- 2026-10-23: 78
- 2026-10-22: 78
- 2026-10-21: 77
- 2026-10-20: 77
- 2026-10-19: 76
- 2026-10-18: 76
- 2026-10-17: 75
- 2026-10-16: 75
- 2026-10-15: 74
- 2026-10-14: 74
- 2026-10-13: 73
- 2026-10-12: 73
- 2026-10-10: 72
- 2026-10-09: 72

## Common questions

### What is the OptimToken MCP server?

OptimToken is an MCP server listed in the public MCP registry as io.github.OptimNow/ai-pricing-hub. Compare LLM API and cloud compute pricing, estimate workload costs, pick a model within budget. This page covers its hosted endpoint (https://optimtoken-mcp.optimnow.io/mcp).

### Is the OptimToken MCP server safe to use?

OptimToken scores 84 out of 100 on VerifyMCP. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.

### What tools does the OptimToken MCP server expose?

OptimToken exposes 5 tools: compare-llm-models, estimate-llm-cost, compare-models-side-by-side, recommend-llm-model, compare-compute-pricing. Their descriptions and schemas cost roughly 2,390 tokens of context every time the server is loaded.

### Does the OptimToken MCP server require authentication?

No. We connected to OptimToken without credentials and it answered, so anything it exposes is reachable by anyone who knows the address.

### Is the OptimToken MCP server still maintained?

OptimToken is still listed as active in the MCP registry. We last reached this channel on 4 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.

## Links

- Remote endpoint: https://optimtoken-mcp.optimnow.io/mcp
- Repository: https://github.com/OptimNow/ai-pricing-hub-mcp
- Website: https://optimtoken.optimnow.io/mcp
- Changelog RSS feed: https://verifymcp.io/servers/optimnow-ai-pricing-hub/optimtoken-mcp.xml
- Changelog JSON feed: https://verifymcp.io/servers/optimnow-ai-pricing-hub/optimtoken-mcp.json
- HTML version of this page: https://verifymcp.io/servers/optimnow-ai-pricing-hub/optimtoken-mcp