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OptimToken

by io.github.OptimNow

Estimate LLM Cost

estimate-llm-cost
Read-only

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. IMPORTANT: Report all cost figures EXACTLY as returned. Do NOT add commentary or recommendations beyond the data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNameNoModel 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.
monthlyVolumeNoExact monthly request count, any integer (default 100,000). This tool does not take the 10k/100k/1m presets the other cost tools use.
useCasePresetNoWorkload 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 / 2k), invoiceProcessing (1.5k / 600, batch-eligible), callSummary (2k / 700, batch-eligible), agentWorkflow (6k / 3k). Default: every preset.
customInputTokensNoCustom 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 eligibility, so its optimized cost equals its list cost.
customOutputTokensNoCustom output tokens per request. Must be supplied TOGETHER with customInputTokens — either alone is ignored and the preset is used.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNo
sourceYes
volumeYes
eloAsOfYes
dataAsOfNo
modelCostsYes
provenanceNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed11 schema fields changed
    • changedInput schema / properties / customInputTokens / description
      Previous value: -"Custom input tokens per request (overrides preset)"New value: +"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 eligibility, so its optimized cost equals its list cost."
    • changedInput schema / properties / customOutputTokens / description
      Previous value: -"Custom output tokens per request (overrides preset)"New value: +"Custom output tokens per request. Must be supplied TOGETHER with customInputTokens — either alone is ignored and the preset is used."
    • changedInput schema / properties / modelName / description
      Previous value: -"Model name to estimate costs for (e.g. 'GPT-4o', 'Claude Sonnet 4'). If omitted, shows top models."New value: +"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."
    • changedInput schema / properties / monthlyVolume / description
      Previous value: -"Custom monthly volume (default: 100,000)"New value: +"Exact monthly request count, any integer (default 100,000). This tool does not take the 10k/100k/1m presets the other cost tools use."
    • changedInput schema / properties / useCasePreset / description
      Previous value: -"Use case preset. Default: all presets."New value: +"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 / 2k), invoiceProcessing (1.5k / 600, batch-eligible), callSummary (2k / 700, batch-eligible), agentWorkflow (6k / 3k). Default: every preset."
    • addedOutput schema / properties / modelCosts / items / properties / costs / items / properties / batchApplied
      Added value: +{
+  "type": "boolean"
+}
    • addedOutput schema / properties / modelCosts / items / properties / costs / items / properties / batchEligible
      Added value: +{
+  "type": "boolean"
+}
    • addedOutput schema / properties / modelCosts / items / properties / costs / items / properties / cacheApplied
      Added value: +{
+  "type": "boolean"
+}
    • addedOutput schema / properties / modelCosts / items / properties / costs / items / properties / cacheEligible
      Added value: +{
+  "type": "boolean"
+}
    • addedOutput schema / properties / modelCosts / items / properties / costs / items / properties / savingsPct
      Added value: +{
+  "type": "number"
+}
    • changedOutput schema / properties / modelCosts / items / properties / costs / items / required
      Previous value: -[
-  "useCase",
-  "inputTokens",
-  "outputTokens",
-  "perRequest",
-  "monthly",
-  "perRequestOptimized",
-  "monthlyOptimized"
-]New value: +[
+  "useCase",
+  "inputTokens",
+  "outputTokens",
+  "perRequest",
+  "monthly",
+  "perRequestOptimized",
+  "monthlyOptimized",
+  "savingsPct",
+  "batchEligible",
+  "cacheEligible",
+  "batchApplied",
+  "cacheApplied"
+]
  2. Changed2 schema fields changed
    • changedOutput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • addedOutput schema / properties / provenance
      Added value: +{
+  "additionalProperties": false,
+  "properties": {
+    "catalogTotal": {
+      "type": "number"
+    },
+    "dataAsOf": {
+      "type": "string"
+    },
+    "eloAsOf": {
+      "type": "string"
+    },
+    "label": {
+      "type": "string"
+    },
+    "notice": {
+      "type": "string"
+    },
+    "pricesVerified": {
+      "type": "boolean"
+    },
+    "source": {
+      "type": "string"
+    },
+    "tier": {
+      "type": "number"
+    },
+    "upstreamSchemaVersion": {
+      "type": "string"
+    },
+    "upstreamSource": {
+      "type": "string"
+    },
+    "upstreamTimestamp": {
+      "type": "string"
+    }
+  },
+  "required": [
+    "tier",
+    "source",
+    "label",
+    "pricesVerified",
+    "eloAsOf"
+  ],
+  "type": "object"
+}
  3. Changed2 schema fields changed
    • changedOutput schema / $schema
      Previous value: -"https://json-schema.org/draft/2020-12/schema"New value: +"http://json-schema.org/draft-07/schema#"
    • removedOutput schema / properties / provenance
      Removed value: -{
-  "additionalProperties": false,
-  "properties": {
-    "catalogTotal": {
-      "type": "number"
-    },
-    "dataAsOf": {
-      "type": "string"
-    },
-    "eloAsOf": {
-      "type": "string"
-    },
-    "label": {
-      "type": "string"
-    },
-    "notice": {
-      "type": "string"
-    },
-    "pricesVerified": {
-      "type": "boolean"
-    },
-    "source": {
-      "type": "string"
-    },
-    "tier": {
-      "type": "number"
-    },
-    "upstreamSchemaVersion": {
-      "type": "string"
-    },
-    "upstreamSource": {
-      "type": "string"
-    },
-    "upstreamTimestamp": {
-      "type": "string"
-    }
-  },
-  "required": [
-    "tier",
-    "source",
-    "label",
-    "pricesVerified",
-    "eloAsOf"
-  ],
-  "type": "object"
-}
  4. Changed2 schema fields changed
    • changedOutput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • addedOutput schema / properties / provenance
      Added value: +{
+  "additionalProperties": false,
+  "properties": {
+    "catalogTotal": {
+      "type": "number"
+    },
+    "dataAsOf": {
+      "type": "string"
+    },
+    "eloAsOf": {
+      "type": "string"
+    },
+    "label": {
+      "type": "string"
+    },
+    "notice": {
+      "type": "string"
+    },
+    "pricesVerified": {
+      "type": "boolean"
+    },
+    "source": {
+      "type": "string"
+    },
+    "tier": {
+      "type": "number"
+    },
+    "upstreamSchemaVersion": {
+      "type": "string"
+    },
+    "upstreamSource": {
+      "type": "string"
+    },
+    "upstreamTimestamp": {
+      "type": "string"
+    }
+  },
+  "required": [
+    "tier",
+    "source",
+    "label",
+    "pricesVerified",
+    "eloAsOf"
+  ],
+  "type": "object"
+}
  5. First observed

TDQS

A5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses important behavioral traits beyond annotations: output includes both list and optimized prices, and instructions to report figures exactly as returned without adding commentary. No contradiction with annotations (readOnlyHint: true, destructiveHint: false).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured and front-loaded: begins with the core purpose, then usage guidance, then parameter details. Every sentence adds value without redundancy. Length is appropriate for the complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the output schema exists, the description covers purpose, usage guidelines, parameter semantics, and behavioral instructions comprehensively. It leaves no gaps for an agent to misinterpret how to invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Adds significant meaning beyond the schema: explains that custom tokens must be supplied together, custom shape assumes no cacheable prefix or batch eligibility, modelName partial matching matches up to 5 models, monthlyVolume default is 100k and doesn't use presets, and useCasePreset defaults to every preset. Schema coverage is 100%, but description compensates with essential context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: costing workloads with exact token counts and monthly volume, distinguishing itself from sibling tools that use preset volumes. It uses specific verbs like 'cost' and 'price' and explicitly names the alternative compare-models-side-by-side.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit guidance on when to use this tool (exact numbers, custom tokens) and when to use a sibling (compare-models-side-by-side for comparing models at preset volumes). It also explains how to use it with model names or across presets.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All OptimToken toolsInspect
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TDQS

A4.7/5.0
Disambiguation5/5

Each tool serves a clearly distinct purpose: cloud compute pricing, LLM catalogue browsing, side-by-side model comparison, custom cost estimation, and model recommendation. The descriptions provide explicit guidance on when to use each tool, eliminating ambiguity.

Naming Consistency5/5

All tool names follow a consistent verb-noun pattern with hyphens (e.g., compare-compute-pricing, estimate-llm-cost). The verbs are imperative and the nouns clearly describe the object, forming a predictable and readable set.

Tool Count5/5

5 tools is well-scoped for the domain of cloud compute and LLM cost comparison. Each tool earns its place by covering a distinct workflow (browsing, comparing specific models, custom costing, recommendation).

Completeness5/5

The tool surface covers the full spectrum of user needs: compute pricing across providers, LLM catalogue exploration, side-by-side comparisons, arbitrary cost estimation, and recommendation with constraints. There are no obvious gaps for the stated purpose of cost analysis.

Resources