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worldbank_indicator

Read-onlyIdempotent

Time series for a World Bank development indicator for one country. Friendly indicators: gdp, gdp_per_capita, gdp_growth, inflation, population, unemployment, life_expectancy, exports, imports, gni_per_capita, poverty_rate, internet_users (or pass a raw World Bank code). Country accepts ISO2/ISO3 codes or common names (e.g. 'US', 'China', 'Germany'). Keyless, official World Bank data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countryNoCountry ISO2/ISO3 code or name (default 'US'). Use 'WLD' for world.
end_yearNoEnd year (optional).
indicatorNoIndicator name (one of: gdp, gdp_per_capita, gdp_growth, inflation, population, unemployment, life_expectancy, exports, imports, gni_per_capita, poverty_rate, internet_users) or a raw WB code.
start_yearNoStart year (optional).

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior, so the bar for extra disclosure is lower. The description adds valuable behavioral context beyond those hints: it is keyless, uses official World Bank data, accepts friendly indicator names or raw World Bank codes, and accepts ISO2/ISO3 codes or common country names.

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 compact, front-loaded with the core purpose, and every sentence contributes useful information. The friendly indicator list and country-format examples are dense but not verbose, and there is no filler or redundant restatement of annotations.

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 simple read-only data retrieval tool with no required parameters and a fully described schema, the description covers the essential call context: data source, keyless access, indicator flexibility, and country input formats. It does not describe the return shape or time-series frequency, but no output schema exists and the purpose is simple enough that this is a minor gap.

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%, so the description need not restate parameter semantics; baseline is 3. It does add a little extra value by giving concrete country examples ('US', 'China', 'Germany') and reaffirming the raw World Bank code option for indicators, but it adds nothing new about start_year or end_year behavior.

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 clearly identifies the tool as returning a time series for a World Bank development indicator for one country, and distinguishes it from worldbank_compare and worldbank_country_profile via the 'one country' scope. It lacks an explicit verb like 'fetches' or 'returns', but the resource and scope are specific and unambiguous.

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 phrase 'for one country' implies this is for single-country indicator series, and listing friendly indicator names provides useful context. However, it does not explicitly name alternatives such as worldbank_compare for cross-country comparisons or worldbank_country_profile for broader country data, nor does it state when not to use this tool.

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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TDQS

B3.2/5.0
Disambiguation2/5

Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.

Naming Consistency3/5

The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.

Tool Count1/5

290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.

Completeness4/5

For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.

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