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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

A4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, covering safety. The description adds the World Bank source and frames the output as 'precise', but does not disclose data freshness, rate limits, or any behavioral quirks. This is adequate but not rich; baseline 3 with 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 concise and well-structured: purpose, indicator list, source, use case, and inputs are each given in one short sentence. Every sentence carries useful information, with no fluff or repetition.

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 an output schema and annotations, the description is sufficient. It covers purpose, indicators, source, and usage trigger. The async behavior is already documented in the schema. Minor gap: no mention of data vintage or update frequency, but not essential.

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% with descriptions for all parameters, so the baseline is 3. The description repeats the indicator list and mentions country code format ('ISO-2 or ISO-3'), which slightly reinforces but does not substantially expand on 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 starts with a specific verb and resource: 'Return a precise macroeconomic indicator for a country'. It explicitly lists the available indicator names, distinguishing it from sibling tools like fx_rate or market_sizing by focusing on country-level macro figures from the World Bank.

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?

Provides clear when-to-use context: 'an agent's analysis needs an authoritative country-level economic figure.' It does not mention when not to use it or explicitly name alternative tools, so it misses the top-tier threshold for exclusion/alternative guidance.

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

C2.4/5.0
Disambiguation1/5

Over 50 tools share the identical template 'Gapup agent-payable C-suite expertise' with similar French descriptions and reference cases, making their boundaries indistinguishable. Clusters like competitor_intel, competitive_deep_dive, competitor_moves, competitor_profiles, competitor_pricing_radar, competitor_pricing_scrape, and competitor_recommendations heavily overlap in purpose.

Naming Consistency1/5

Names are chaotic: mix of French and English, snake_case and camelCase, verb_noun, noun, and adjective forms with no uniform pattern. Examples like 'bp_narratif', 'content_enrichment', 'ai_governance_full_report_async', and 'job_result' show no coherent naming convention.

Tool Count1/5

271 tools is far beyond any reasonable MCP server scope, creating an overwhelming selection burden for agents. This count vastly exceeds the 25+ threshold for 'too many' and makes navigation impractical.

Completeness2/5

While the server covers many business domains, it lacks lifecycle operations (e.g., no update/delete tools for the deliverables it generates) and the input specifications are vague ('documented case fields' without documentation), creating functional dead ends. The sheer breadth does not compensate for these gaps.