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economic_indicator

Read-only

Return a precise macroeconomic indicator for a country — the exact figure for a market-sizing, finance or strategy workflow. Indicators: gdp_usd, gdp_per_capita, gdp_growth, inflation, unemployment, population. Source: World Bank. When to use: an agent's analysis needs an authoritative country-level economic figure. Inputs: country (ISO-2 or ISO-3 code) and indicator name.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asyncNoIf true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
countryYesCountry code, ISO-2 or ISO-3 (e.g. FR, USA)
indicatorYesMacroeconomic indicator name

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearYes
valueYes
sourceYes
countryYes
indicatorYes
source_urlNo
indicator_codeNo

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint=true and openWorldHint=true. The description adds valuable context: source is World Bank, returns 'the exact figure,' and implies authoritative data. No contradictions. The description enhances understanding of behavior beyond annotations.

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 two sentences plus a list, front-loaded with the main purpose. Every sentence is useful and concise, with no extraneous information.

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?

Given the output schema exists, return values are covered. Parameters are fully described in schema. The description adds source and usage context. For a simple lookup tool, it is sufficiently complete, though data freshness is not mentioned.

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 coverage is 100%, so baseline is 3. The description repeats the indicator names and country code format already in the schema, adding minimal new meaning. The 'async' parameter is not mentioned, but it is covered in schema. Overall, the description adds marginal value.

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 returns a precise macroeconomic indicator for a country, lists specific indicators, and mentions the source (World Bank). It distinguishes itself from siblings like fx_rate or interest_rate by focusing on broad economic figures for market-sizing, finance, or strategy workflows.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'When to use: an agent's analysis needs an authoritative country-level economic figure,' providing clear context. However, it does not state when not to use it or mention alternative tools for related but different data (e.g., exchange rates).

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

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

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