Agent Pricing Index

judgment_multiple

FREE. The Judgment Multiple: annualized exposure divided by the annualized cost of the human control that guards it. Tells you whether a human reviewer on an AI workflow is cheap insurance or overhead.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "annual_exposure_usd": "<annual_exposure_usd>",
      "annual_control_cost_usd": "<annual_control_cost_usd>"
    },
    "name": "judgment_multiple"
  }
}

Input Schema

Name	Required	Description	Default
`annual_exposure_usd`	Yes	Dollars at risk per year if the AI output is wrong and nobody catches it.
`annual_control_cost_usd`	Yes	What the human check costs per year.

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 27, 2026

TDQS

A3.7/5.0

Behavior3/5

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

No annotations are provided, so the description carries the burden; it does note the tool is FREE and purely computational, and the formula fully determines behavior. It omits edge behavior such as division by zero when control cost is 0 and whether the result is dimensionless, which matter for a ratio tool with no output schema.

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 waste, with the 'FREE' cost cue and the formula front-loaded ahead of interpretation. Every sentence earns its place.

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

Completeness3/5

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

For a two-parameter pure calculation with full schema coverage this is mostly sufficient, but with no output schema the description never says what the return value looks like or what threshold separates 'insurance' from 'overhead'. That interpretive gap is the main missing piece.

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 coverage is 100% and both parameters are already documented with plain-language definitions. The description explains the ratio's business meaning but adds no format, unit, or bounds information beyond the schema, so baseline 3 is right.

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?

It states an exact computation (annualized exposure divided by annualized control cost) and the interpretation it produces (cheap insurance vs overhead). Nothing in the fde_* or pricing_* siblings overlaps, so an agent can route to it unambiguously.

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

Usage Guidelines3/5

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

The framing 'whether a human reviewer on an AI workflow is cheap insurance or overhead' implies the use case, but there is no explicit when-to-use, when-not, or pointer to a sibling. Usage is inferred rather than stated.

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

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