Model Ruler — AI Cost Calculators

Agent Workflow Platform Cost Calculator

agent-workflow-cost-calculator

Read-onlyIdempotent

Use for automation-platform/iPaaS cost of an AI agent workflow, including app-action fan-out and MCP tool-call accounting, explicitly excluding LLM token spend. Use automation-cost-calculator for generic non-agent workflow billing. Use agent-loop-cost-calculator for LLM inference/reasoning cost per successful multi-step agent task.

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": {
      "agent_runs_per_month": 20000
    },
    "name": "agent-workflow-cost-calculator"
  }
}

Input Schema

Name	Required	Description
`app_actions_per_run`	No	Downstream app actions per agent run (default 3)
`agent_runs_per_month`	Yes	Agent workflow runs per month
`make_modules_per_run`	No	Make modules per run; defaults to app actions + MCP calls
`mcp_tool_calls_per_run`	No	MCP tool calls per agent run (default 1)

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 6, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already establish this as a safe, read-only, idempotent computation, so the description only needs to add scope. It usefully discloses the cost boundaries — includes app-action fan-out and MCP tool calls, excludes LLM token spend — which materially affects interpretation of the result. It does not state units, currency, or output shape, so it falls short of a 5.

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?

Three tight sentences, front-loaded with the core purpose, then the two exclusions/routes. Every clause carries routing or scoping information with no filler.

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

Completeness4/5

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

For a deterministic calculator with full schema coverage and annotations covering the safety profile, the description supplies everything needed to select and invoke it correctly. The only minor gap is that it never indicates what the returned cost value looks like or its units, and no output schema exists to fill that in.

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 all four parameters (including defaults for app_actions_per_run, make_modules_per_run, mcp_tool_calls_per_run) are documented inline. The description's mention of app-action fan-out and MCP accounting reinforces but does not extend the schema, so the baseline 3 applies.

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?

States a specific verb (cost calculation) and resource (automation-platform/iPaaS spend for an AI agent workflow), and scopes it precisely with what is included (app-action fan-out, MCP tool-call accounting) and excluded (LLM token spend). This immediately separates it from its siblings without opening any schema.

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?

Explicitly names two alternatives with the condition that selects each: 'automation-cost-calculator for generic non-agent workflow billing' and 'agent-loop-cost-calculator for LLM inference/reasoning cost'. This is textbook when-to-use/when-not-to-use routing.

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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