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inflation_calc

Adjust historical money amounts for inflation to find their equivalent value in any year, using country-specific data for the US, UK, Germany, or France.

Instructions

Calculate the inflation-adjusted value of money over time.

Parameters:
    amount — Original amount of money.
    from_year — Starting year (e.g. 2000).
    to_year — Target year (e.g. 2026).
    country — Country code for inflation data: 'US' (default), 'UK', 'DE', 'FR'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
amountYes
from_yearYes
to_yearYes
countryNoUS

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It merely states the calculation purpose without explaining data sources, accuracy, or any side effects. For a simple calculator, this might suffice, but it lacks necessary transparency for agents to understand reliability or dependencies.

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?

The description is extremely concise: one sentence for purpose plus a structured parameter list. Every line contributes value without redundancy, making it easy for an agent to parse quickly.

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?

The description covers the parameters well but does not explain the return format or any limitations (e.g., year ranges, inflation data sources). However, since an output schema exists, the agent can infer return structure. For a simple calculator, this is moderately complete.

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

Parameters4/5

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

With 0% schema description coverage, the description adds significant meaning by listing each parameter with a brief explanation and example values for years and country codes. This helps agents understand the expected input beyond the schema's bare types.

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 uses a clear verb-resource pair: 'Calculate the inflation-adjusted value of money over time.' This immediately conveys the tool's primary function and distinguishes it from sibling tools like amortization_calc or compound_interest, which serve different financial calculations.

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 description provides parameters and defaults, which implicitly guides usage, but it does not explicitly state when to use this tool versus alternatives, nor does it mention any prerequisites or exclusions. For a straightforward calculator, this is adequate but not exemplary.

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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