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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).

Schema Changelog

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

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already establish readOnly, idempotent, non-destructive behavior, so the bar is lower. The description adds useful context beyond annotations by stating the data is 'Keyless, official World Bank data' and that the output is a time series. This gives the agent confidence about data source and no authentication requirement.

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 with the core purpose. The indicator list adds helpful examples but partially duplicates the schema's indicator enumeration. Overall it is efficient, with no filler or redundant safety language.

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-fetch tool with full schema coverage and no output schema, the description is largely complete. It explains the data source, keyless access, country and indicator flexibility, and the one-country time series scope. It does not detail default year ranges or response formatting, but those are minor given the simplicity.

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 schema already documents all four parameters well. The description adds friendly examples and context like common country names and raw World Bank codes, but these largely restate what the schema already provides. It does not materially expand parameter semantics 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 returns time series for a World Bank development indicator for one country, which is a specific verb+resource combination. The phrase 'for one country' differentiates it from sibling tools like worldbank_compare and worldbank_country_profile, even without naming them.

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 clearly frames when to use this tool: when you need a single-country time series for a development indicator. It also gives friendly indicator names and country input formats, which helps the agent select it. However, it does not explicitly mention sibling alternatives or state when not to use it, such as when comparing multiple countries.

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

B3.3/5.0
Disambiguation2/5

Several tool clusters overlap heavily—company due-diligence and risk tools (counterparty_risk_score, company_trust_check, entity_dossier, issuer_diligence_dossier, resolve_entity, entity_resolve), carrier vetting tools, sanctions screening tools, and recall tools all have subtle boundary distinctions. While descriptions are detailed, an agent navigating 294 tools will frequently struggle to pick the right one.

Naming Consistency3/5

Most tools follow a readable snake_case domain-prefix pattern (fdic_, edgar_, sanctions_, congress_), which helps. However, verb placement is inconsistent—search_available_datasets vs cdc_dataset_query, resolve_entity vs entity_resolve—and synonyms like search, lookup, get, detail, fetch, and status are used interchangeably.

Tool Count1/5

294 tools is an extreme number for a single MCP server, far beyond what an agent can reliably hold in context or select from accurately. The presence of tool-group discovery helpers mitigates but does not solve the fundamental scale problem.

Completeness4/5

The data breadth is genuinely extensive, covering finance, health, legal, real estate, transportation, energy, cyber, education, and many other domains, often with generic query fallbacks. Still, some capabilities are shallow or incomplete—package tracking stops at a link, property tools are demo-only in places, and caselaw coverage is limited—so it is not a fully complete surface.