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kgy0617

Global Economic Statistical MCP

by kgy0617
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Explain indicator

explain_indicator
Read-onlyIdempotent

Find out what any economic indicator means, how it is measured, and which official sources provide it per country, plus methodology and related tables for Korean statistics.

Instructions

Explain what an indicator measures and which sources (institution, dataflow, key) serve each economy.

Returns the concept definition, its source mappings with rendered keys, and for Korean statistics the
Bank of Korea glossary definition, methodology notes and related ECOS tables.

Args:
    term: indicator or term (e.g. "CPI_YOY", "policy rate", "unemployment", "경제심리지수")
    country: economy whose sources to show (default: every verified economy)
    stat_code: a specific ECOS table to explain
    language: "kr" | "en" (Bank of Korea glossary and methodology)

Returns:
    concept (definition, unit, sources), sources per economy, definition (glossary), methodology, tables

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
termYes
countryNo
languageNokr
stat_codeNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so safety is covered. The description adds behavioral detail about the return content (concept, sources, glossary, methodology, tables), which enriches what the agent can expect beyond the annotations. No contradictions.

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 compact and well-structured: a one-sentence purpose, then a returns summary, then an Args block with examples. Front-loaded with the core purpose and no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only tool with four parameters and no output schema, the description explains all parameters, provides examples, and lists the return fields. It is complete enough for an agent to call it correctly without further inference.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description's 'Args' section explains each parameter (term, country, stat_code, language) with examples and defaults. It fully compensates for the missing schema descriptions, providing meaning beyond the raw property names.

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 states a specific verb ('explain') and resource ('indicator'), and specifies what it returns: concept definition, source mappings, and for Korean statistics, glossary, methodology, and tables. This clearly distinguishes it from siblings like get_data or get_metadata.

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?

It gives contextual hints (e.g., 'for Korean statistics') but does not explicitly state when to use this tool versus alternatives like search_statistics or get_metadata. No exclusions or when-not-to-use guidance is provided.

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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