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Glama

Global Economic Data

get_global_gdp

Get global GDP and macroeconomic indicators from the IMF World Economic Outlook. Supports GDP growth, inflation, government debt, current account, and GDP per capita for major economies.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNoYear for data (default: current year)2026
countriesNoComma-separated ISO-3 country codes (default: USA,CHN,DEU,GBR,JPN,FRA,IND,BRA)USA,CHN,DEU,GBR,JPN,FRA,IND,BRA
indicatorNoIMF indicator code: NGDP_RPCH (GDP growth), PCPIPCH (inflation), GGXWDG_NGDP (gov debt), BCA_NGDPD (current account), NGDPDPC (GDP per capita)NGDP_RPCH

Schema Changelog

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

  1. First observed

TDQS

B3.4/5.0
Behavior2/5

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

No annotations are provided, so the description must cover behavioral traits. It indicates a read-like operation (getting data) but does not disclose aspects like rate limits, data freshness, or whether it is truly read-only. The description is minimal beyond listing indicators.

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 concise at two sentences, with the first sentence front-loading the purpose and the second listing supported indicators. It is clear and efficient, though a slight improvement could include a brief usage note.

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 what the tool does and the main data types, but given the lack of output schema, it omits return format or structure. For 3 parameters and no annotations, it is adequate but not fully complete – the agent may need more info on what the response looks like.

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 baseline is 3. The description adds some context by listing the types of indicators (GDP growth, inflation, etc.), but the schema already provides detailed descriptions including default values and indicator codes. The description does not significantly add meaning beyond the schema.

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 clearly states the tool gets global GDP and macroeconomic indicators from the IMF World Economic Outlook, specifying the data types (GDP growth, inflation, etc.). It distinguishes from siblings by naming the source (IMF WEO), which differentiates it from get_oecd_indicators and get_world_bank_data.

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 usage for IMF data but does not explicitly state when to use this tool versus the siblings. It lists supported indicators and major economies, but lacks guidance on exclusions or context for selecting this over alternatives.

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

A3.7/5.0
Disambiguation4/5

The tools are mostly distinct by data source (IMF, OECD, World Bank), but there is overlap in indicators like GDP, which could cause confusion. Descriptions help clarify the focus of each tool.

Naming Consistency5/5

All tool names follow a consistent 'get_SOURCE_WHAT' pattern using snake_case, making them predictable and easy to understand.

Tool Count4/5

Three tools for global economic data from major institutions is reasonable, though slightly minimal given the broad domain.

Completeness3/5

The tools cover key data sources but lack finer control such as country selection, date ranges, or additional economic indicators, leaving notable gaps.

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