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kgy0617

Global Economic Statistical MCP

by kgy0617
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Get structure (metadata)

get_metadata
Read-onlyIdempotent

Inspect a dataset's dimensions and codelists to find valid codes for building series keys before retrieving economic data.

Instructions

Return the structure (dimensions and codelists) of a dataset, to find the codes get_data needs.

- OECD, IMF, BIS: source + dataflow → the dimensions and codelists of the institution's own DSD.
  A series key is the codes joined with '.' in dimension order (e.g. BIS WS_CBPOL → 'M.US').
- ECOS: stat_code → the table's structure mapped to SDMX (FREQ + ITEM_CODE1..4, coverage, units).
  output_format="sdmx" returns an SDMX-JSON structure message.

Args:
    stat_code: ECOS table code (e.g. "901Y009")
    source: OECD | IMF | BIS (with dataflow)
    dataflow: SDMX dataflow (e.g. "BIS:WS_CBPOL(1.0)", "IMF.STA:CPI")
    code_keyword: filter codes by name or value (e.g. "Japan", "JPN", "current account")
    codes_limit: maximum codes per dimension (default 30)
    output_format: "compact" | "sdmx" (ECOS only)
    language: "kr" | "en" (ECOS names)

Returns:
    dimensions, codes per dimension, a key template

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceNo
dataflowNo
languageNokr
stat_codeNo
codes_limitNo
code_keywordNo
output_formatNocompact

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds valuable behavioral context beyond annotations by detailing source-specific behavior, the series-key format (e.g., 'M.US'), and the output_format='sdmx' effect for ECOS. It supplements the annotations without contradicting them.

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 tightly organized: a one-sentence purpose statement, two concise source-specific behavior bullets, and a clean parameter list. Every sentence adds value—the series-key example is particularly illustrative without being verbose. No filler or redundant content.

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 there is no output schema, the description adequately explains returns ('dimensions, codes per dimension, a key template') and covers the special output_format behavior. It also addresses both usage modes and parameter semantics. It stops short of detailing error conditions or required-parameter validation, but for a read-only metadata lookup tool, the coverage is sufficient for correct invocation.

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%, so the description is the only source of parameter meaning. It compensates thoroughly with an Args list that explains every parameter, including acceptable values (source, output_format), examples (stat_code '901Y009', dataflow 'BIS:WS_CBPOL(1.0)'), and cross-parameter interactions. This is essential and well-executed.

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-resource pair: 'Return the structure (dimensions and codelists) of a dataset', and explicitly ties its purpose to 'to find the codes get_data needs.' This clearly differentiates it from sibling tools like get_data and search_statistics, making the tool's role obvious.

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 provides clear contextual guidance: use this tool to obtain dimension/codelist codes before calling get_data. It explains the two distinct usage modes (OECD/IMF/BIS with source+dataflow, ECOS with stat_code) and gives concrete examples. However, it does not explicitly discuss when not to use the tool or how it compares to alternatives like search_statistics or explain_indicator, so it stops short of full exclusion 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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