Model Ruler — AI Cost Calculators

LLM Provider Cost Calculator

provider-cost-calculator

Read-onlyIdempotent

Use when a user asks what an LLM workload costs on a specific provider/model, or wants to compare cost across providers. Given tokens per call and call volume, returns monthly cost plus a tier comparison table.

Example tool call

This call uses example arguments supplied in the tool’s input schema.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "tokens_in": 1200,
      "tokens_out": 400,
      "calls_per_month": 100000
    },
    "name": "provider-cost-calculator"
  }
}

Input Schema

Name	Required	Description
`model`	No	Target model (e.g. claude-sonnet-4-6)
`provider`	No	Target provider (e.g. anthropic, openai, together)
`tokens_in`	Yes	Input tokens per call
`tokens_out`	Yes	Output tokens per call
`calls_per_month`	No	Monthly call volume
`include_comparison`	No	Include tier comparison table (default true)

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 6, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive, and closed-world behavior, so the safety profile is covered. The description adds real behavioral value by disclosing the return content (monthly cost plus a tier comparison table), which matters because there is no output schema. It does not mention default assumptions for optional inputs beyond include_comparison, but the added context is meaningful.

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?

Two sentences, zero filler, front-loaded with the 'Use when' trigger followed immediately by the input-to-output contract. Every clause earns its place.

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?

With no output schema, the description must convey what comes back, and it does (monthly cost plus tier comparison table). All six parameters are fully documented in the schema and the safety annotations are complete, so nothing an agent needs to invoke this correctly is missing.

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

Parameters3/5

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

Schema description coverage is 100% and each of the six parameters is documented inline with units and examples ('Input tokens per call', 'Monthly call volume'), so the schema carries the load. The description only restates tokens-per-call and call volume at a high level and adds no syntax or default detail beyond what the schema provides, matching the baseline 3.

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

Purpose4/5

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

States a specific verb and resource: given tokens per call and call volume, it returns monthly cost plus a tier comparison table. Distinguishes itself implicitly from siblings like rag-pipeline-cost-calculator or agent-loop-cost-calculator by targeting a raw provider/model workload, but it never names or contrasts those alternatives explicitly, so the differentiation is left to inference.

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

Usage Guidelines4/5

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

Gives clear triggering conditions: 'when a user asks what an LLM workload costs on a specific provider/model, or wants to compare cost across providers.' That is a solid when-to-use statement, but it offers no when-not guidance and does not route the agent to or away from any of the ten sibling calculators that also compute cost.

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

All Model Ruler — AI Cost Calculators tools Inspect

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