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pich

ai-economics-mcp

by pich

agent_hour

Calculate the fully-loaded cost of one AI agent-hour, including compute and human verification, compared to the human hour it replaces.

Instructions

Agent-Hour Cost: Fully-loaded cost of one AI agent-hour: compute plus human verification, vs the human hour it replaces. All parameters optional — defaults mirror the interactive calculator at https://piszczek.pl/tools/agent-hour. The response includes result, formula, interpretation and a ready-to-quote cite_as sentence.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
priceNoblended $/1M tokens (default 6)
tokens_mNoMtok consumed per agent-hour (default 1.5)
human_rateNohuman $/h it replaces (default 60)
review_minNohuman verification minutes per agent-hour (default 15)

Schema Changelog

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

  1. First observedv1.0.2

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, the description carries the full burden; it discloses the defaulting behavior (mirrors interactive calculator), confirms all parameters optional, and enumerates the response contents (result, formula, interpretation, cite_as sentence). It does not discuss edge cases or errors, but for a pure calculation tool this is meaningful transparency.

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 short sentences; the definition is front-loaded, the default/use guidance sits in the middle, and the output contract ends it. There is 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?

The tool has only four fully documented optional numeric parameters and no output schema. The description fills the output gap by naming the four response pieces, and the calculator link anchors expected behavior. It could have spelled out the formula or units explicitly, but those are already implied by parameter descriptions and the response contract.

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%: each parameter already has type, semantics, and a default value. The description adds that all are optional and that defaults come from the linked calculator, but it does not add per-parameter meaning, so the baseline 3 is appropriate.

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?

The description names the exact resource ('fully-loaded cost of one AI agent-hour'), defines its components (compute plus human verification), and frames the comparator (vs the human hour it replaces). It is not a tautology, but it never uses an imperative verb and does not explicitly differentiate from the sibling cost/energy tools, so it stops one step short of a 5.

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?

It says all parameters are optional and defaults mirror the online calculator, which is actionable invocation guidance, and the 'vs the human hour it replaces' clause implies the intended use case. However, it does not specify when to prefer this over sibling tools like token_cost or llm_energy, nor any exclusions, so the usage guidance is implied rather than explicit.

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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