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kgy0617

Global Economic Statistical MCP

by kgy0617
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Calculate statistics

calculate_statistics
Read-onlyIdempotent

Compute summary statistics for macroeconomic data series, including min/max, mean, median, std, change, CAGR, trend, drawdown, and YoY metrics, with validation and provenance.

Instructions

Summary statistics of a series (addressed the same way as get_data).

Per series: count, first/last, min/max (with periods), mean, median, std, change/change_pct, cagr_pct,
trend_per_year/trend_r2, latest_pop_pct, pop_pct_std, max_drawdown_pct, latest_yoy_pct, mean_yoy_pct.
Rates (interest rates, inflation) change in percentage points. Validation and provenance are included.

Args:
    indicator: concept id or name
    country: economy, ISO code or EA (required with indicator)
    source: ECOS | OECD | IMF | BIS
    stat_code: ECOS table code
    cycle: frequency
    item_code1..4: ECOS item codes
    dataflow: SDMX dataflow
    key: SDMX series key
    start_date: start period
    end_date: end period
    recent_years: window

Returns:
    statistics, validation status and provenance per series

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyNo
cycleNo
sourceNo
countryNo
dataflowNo
end_dateNo
indicatorNo
stat_codeNo
item_code1No
item_code2No
item_code3No
item_code4No
start_dateNo
recent_yearsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A4.2/5.0
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, covering safety. The description adds behavioral context: it notes that rates change in percentage points, that validation and provenance are included, and lists the full set of statistics. It does not contradict annotations and adds useful nuance beyond the structured fields.

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 structured with a clear purpose statement, a compact list of statistics, and a labeled Args section. It is longer than minimal but each part earns its place, especially the param hints. The front-loading of purpose is effective.

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 tool with 14 parameters and no output schema, the description covers the return value ('statistics, validation status and provenance per series') and the addressing method (same as get_data). It does not explain how to choose among the many addressing options (indicator vs. stat_code vs. dataflow), but given the complexity, it provides enough context for an agent to call it correctly, especially with sibling tools available.

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 must compensate. It does: every one of the 14 parameters is explained with a brief type or format hint (e.g., 'concept id or name', 'economy, ISO code or EA', 'ECOS | OECD | IMF | BIS', 'frequency', 'SDMX series key'), and it even notes a dependency ('required with indicator'). This adds substantial meaning beyond the bare 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 states a specific verb ('calculate') and resource ('summary statistics of a series'), and enumerates the exact statistics returned (count, min/max, mean, median, std, CAGR, etc.). It also references get_data for addressing, which helps distinguish it from sibling tools like get_data and compare_series.

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?

The description implies usage when summary statistics are needed, and mentions 'addressed the same way as get_data' which hints at a parallel, but it does not explicitly state when to use this tool over siblings like get_data or compare_series, nor provide exclusions or conditions. Guidance is implicit rather than explicit.

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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