inflation_adjustConvert a nominal amount between years using official CPI: 'what is 100 in 1995 money worth in today's money?' Works for any country with CPI data (default USA). Returns the adjusted amount, the exact index values and formula used, and the citation. Annual-average precision.
| Name | Required | Description | Default |
|---|---|---|---|
| amount | Yes | ||
| country | No | Default 'USA'. | |
| to_year | Yes | ||
| from_year | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds meaningful behavioral details beyond the annotations: returns adjusted amount, exact index values, formula, citation, and specifies annual-average precision. This enriches the agent's understanding of expected output and processing, with no contradiction to annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences that are dense with useful information, front-loaded with the primary verb and example. No fluff or redundancy; every phrase adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite lacking an output schema, the description clearly states what will be returned and precision. It doesn't cover edge cases like invalid years or missing CPI data, but for a straightforward conversion tool, the level of detail is adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is low (25% only for country). The description gives an illustrative example but doesn't elaborate on the meaning/format of amount, from_year, or to_year beyond the context. It partially compensates but leaves gaps for required numeric parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: converting a nominal amount between years using official CPI, with a concrete example. It distinguishes itself from siblings like fx_convert (currency conversion) by explicitly focusing on inflation adjustment across years.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear context via the example and notes that it works for any country with CPI data (default USA). However, it doesn't explicitly mention when to prefer this over alternatives like fx_convert, relying on implicit distinction.
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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Tools are organized into clear functional groups (retrieval, search, verification, conversion), and pairs like get_indicator/get_series and verify_stat/verify_claims have explicit usage distinctions. The only minor overlap is between fetch and get_series, but their descriptions clarify when to use each.
Most tool names follow a verb_noun snake_case pattern (get_indicator, verify_stat, compare_sources), with a few single-word or noun-style exceptions (fetch, search, country_snapshot). The overall convention is predictable and consistent.
12 tools is within the optimal range for a domain of this complexity, covering retrieval, search, comparison, verification, conversion, and source listing without unnecessary bloat.
The surface covers the full workflow: discover indicators (search_indicators), search data (search), fetch data (get_indicator, get_series, fetch, country_snapshot), compare sources (compare_sources), convert values (fx_convert, inflation_adjust), verify claims (verify_stat, verify_claims), and understand sources (list_sources). No critical gaps are apparent.