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Glama

country_indicator

Get a macroeconomic indicator for a country over recent years (World Bank). Indicators: gdp, gdp_per_capita, gdp_growth, inflation, unemployment, population, life_expectancy, exports, imports, govt_debt_pct_gdp, real_interest_rate, fdi, co2_per_capita, internet_users. Country accepts an ISO code (US, DE, JP) or a World Bank country code. You may also pass a raw World Bank indicator code.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearsNoHow many of the most recent years to return.
countryYesCountry as an ISO code or name, e.g. US, USA, or United States.
indicatorYesWorld Bank indicator key: gdp, gdp_per_capita, gdp_growth, inflation, population, unemployment, life_expectancy, exports, imports, govt_debt_pct_gdp, real_interest_rate, fdi, co2_per_capita, internet_users. Call list_indicators for the full set. Raw World Bank codes are also accepted.

Schema Changelog

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

  1. First observed

TDQS

B3.4/5.0
Behavior3/5

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

No annotations are present, so the description carries the transparency burden. It discloses that the tool returns recent-years data, accepts ISO/World Bank country codes, and supports raw World Bank indicator codes. However, it does not describe the output shape, units, missing-data behavior, or data freshness, which is a notable gap for a data lookup 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded, stating the core purpose first and then listing supported indicators and input variations. The indicator enumeration is long but useful for quick scanning, and there is no filler or redundant explanation.

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?

With no output schema and no annotations, the description should clarify what a successful call returns. It indicates recent-years data and enumerates inputs, but it does not state the return format, units, or potential quirks like missing years or unavailable indicators, and it does not mention the years default. This leaves a moderate completeness 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 baseline is 3. The description mostly restates the indicator list and country-format guidance already present in the schema; the extra mention of 'World Bank country code' adds only marginal clarity beyond 'ISO code or name.' It provides no additional semantics for the years parameter.

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 opens with a clear verb-resource pair: 'Get a macroeconomic indicator for a country over recent years (World Bank)' and enumerates the supported indicators. It is easy to tell this is a single-country data lookup, but it does not explicitly distinguish itself from siblings like compare_countries or us_series.

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 implies when to use it: anytime you need a single country's macroeconomic indicator over recent years. However, it provides no explicit guidance about alternatives, such as using compare_countries for cross-country comparisons or list_indicators to discover all available indicator codes, so usage guidance remains only implicit.

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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