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Satyampatel779

country-analytics-mcp

rank_countries

Rank countries in a continent by GDP, population, or GDP per capita. Get top-N or bottom-N results with excluded countries listed and reasons.

Instructions

Top-N (or bottom-N with order='asc') countries of a continent by gdp, population, or gdp_per_capita. Same scope/year/currency/fx_date semantics as region_total. Countries without data for the requested year are excluded and listed with reasons.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNolatest
orderNodesc
scopeNocontinent
top_nNo
metricNogdp
fx_dateNo
currencyNoUSD
continentYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It does disclose a genuinely useful behavior beyond the schema: countries lacking data for the requested year are excluded and reported with reasons, so partial results are expected. It says nothing about permissions, rate limits, or pagination/limits on top_n.

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?

Three tight sentences with the ranking mode and metric options front-loaded and no filler. The cross-reference to region_total is economical but forces the agent to look up another definition to resolve currency/fx_date semantics.

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?

For an 8-parameter tool with no annotations and no output schema, the description covers the ranking behavior and partial-result handling, including what gets listed alongside results. It leaves currency/fx_date semantics external and gives no sense of the result shape beyond the exclusion list.

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 0% across 8 parameters, so the description must compensate. It adds real meaning for metric (three named measures), order ('asc' flips to bottom-N), and top_n ('Top-N'), but currency and fx_date are only name-dropped by deference to region_total rather than explained, and year/scope semantics are delegated to another tool's description.

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 names a specific operation (Top-N/bottom-N ranking), the resource (countries of a continent), and the valid metrics (gdp, population, gdp_per_capita), which separates it cleanly from siblings like get_country_metric (single value) and region_total (aggregate). An agent can tell what this tool returns without opening the schema.

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?

It explicitly tells the agent how to invert the ranking (order='asc' for bottom-N) and that scope/year/currency/fx_date behave as in region_total, which orients usage. It stops short of stating when NOT to use it or which sibling to prefer for non-continent rankings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.