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kgy0617

Global Economic Statistical MCP

by kgy0617
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Compare series and cross-validate

compare_series
Read-onlyIdempotent

Compare 2-6 economic series across economies, institutions, or concepts by aligning them to one frequency. Get correlations, provenance, and validation in a single output.

Instructions

Align 2-6 series to one frequency and compare them: across economies, institutions or concepts.

Examples:
- economies: [{"indicator":"POLICY_RATE","country":"US"}, {"indicator":"POLICY_RATE","country":"EA"}]
- institutions: [{"indicator":"CPI","country":"CN","source":"IMF"}, {"indicator":"CPI","country":"CN","source":"BIS"}]
  → the same concept and economy from different institutions adds cross_validation automatically
- concepts: [{"indicator":"POLICY_RATE","country":"GB"}, {"indicator":"CPI_YOY","country":"GB"}]

Args:
    series: list of series (indicator/country/source, or stat_code/..., or source/dataflow/key/cycle)
    start_date: start period (default: the last recent_years years)
    end_date: end period
    recent_years: window (default 3)
    frequency: comparison frequency (default: the lowest one)
    aggregation: "mean" | "last" | "first" | "sum" (default per concept: flows such as the current account
        or GDP are summed, stocks such as reserves take the period end, everything else is averaged)
    normalize_method: "none" | "index" (first period = 100) | "zscore"
    join: "inner" | "outer"
    output_format: "compact" | "csv"

Returns:
    the aligned table, correlations, provenance and validation per series, and cross-validation where it applies

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
joinNoinner
seriesYes
end_dateNo
frequencyNo
start_dateNo
aggregationNo
recent_yearsNo
output_formatNocompact
normalize_methodNonone

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description goes further by explaining the alignment process, aggregation defaults based on flow vs. stock concepts, normalization options, and the return payload (table, correlations, provenance, validation). This exceeds the annotation coverage and gives the agent a full picture of side effects and output structure.

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 lengthy but well-structured with a clear purpose line, three illustrative examples, and a bulleted Args list. Every sentence adds value, and the most critical information (alignment and comparison scope) is front-loaded. It could be tightened slightly, but the organization makes it easy to scan.

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?

Despite no output schema, the description explicitly states what is returned: aligned table, correlations, provenance, validation, and cross-validation where applicable. The examples cover the three main use cases, and all parameters and their effects are described. An agent has everything needed to invoke the tool correctly and interpret results.

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?

With schema description coverage at 0%, the description carries the full burden. It lists every parameter, explains defaults (e.g., recent_years=3, aggregation rules), and describes the three acceptable series specifications (indicator/country/source, stat_code, or dataflow/key/cycle). It even provides concrete JSON examples. This is exemplary compensation for the sparse 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?

States a specific verb ('align', 'compare') with a clear resource ('2-6 series') and scope ('across economies, institutions or concepts'). The description distinguishes itself from siblings like get_data and calculate_statistics by focusing on comparison and cross-validation. Examples reinforce the purpose without ambiguity.

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 explicit usage scenarios via three example types (economies, institutions, concepts) and explains the automatic cross-validation when the same concept comes from different sources. It does not explicitly state when not to use it or name alternative tools, but the context is clear enough for an agent to infer appropriate use.

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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