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by com.autario
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seasonality_decomposition

Read-onlyIdempotent

Additive decomposition Y = trend + seasonal + residual. Use this to strip the seasonal cycle from a series and reveal the underlying trend | great for monthly or quarterly data (retail sales, unemployment). Returns per-timepoint components + summary amplitude. Runs on any verified autario indicator (World Bank, FRED, Eurostat, OECD, IMF, WHO, ECB, US Census, SEC).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fullNoReturn the full raw time series (heavy, many tokens). Default false → you get only the summary/stats, which is enough to ANSWER a question. Set true only when you must plot or export every point.
timeNoOptional time window. "2010-2023" keeps 2010 through 2023, "2019-01-2024-06" works by month, and a single "2015" keeps 2015 onward. Omitted or unparseable = the whole series.
entityYesEntity the series is read for: an ISO 3166 alpha-3 country code such as "DEU" or "USA", or an aggregate code such as "WLD" or "EUU" (the canonical name matches too). Use the codes list_indicators and get_entity_profile accept.
periodNoSeasonal period in time steps (12=monthly, 4=quarterly, 7=weekly). Auto-inferred from indicator frequency if omitted.
indicatorYesIndicator id of a numeric series, exactly as list_indicators or get_entity_profile return it (a public indicator or one of your own uploads or connector tables).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • addedInput schema / properties / entity / description
      Added value: +"Entity the series is read for: an ISO 3166 alpha-3 country code such as \"DEU\" or \"USA\", or an aggregate code such as \"WLD\" or \"EUU\" (the canonical name matches too). Use the codes list_indicators and get_entity_profile accept."
    • addedInput schema / properties / indicator / description
      Added value: +"Indicator id of a numeric series, exactly as list_indicators or get_entity_profile return it (a public indicator or one of your own uploads or connector tables)."
    • addedInput schema / properties / time / description
      Added value: +"Optional time window. \"2010-2023\" keeps 2010 through 2023, \"2019-01-2024-06\" works by month, and a single \"2015\" keeps 2015 onward. Omitted or unparseable = the whole series."
  2. Changed1 schema field changed
    • addedInput schema / properties / full
      Added value: +{
+  "description": "Return the full raw time series (heavy, many tokens). Default false → you get only the summary/stats, which is enough to ANSWER a question. Set true only when you must plot or export every point.",
+  "type": "boolean"
+}
  3. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive, so the safety profile is covered. The description adds real value beyond them: what is returned (per-timepoint components plus summary amplitude) and the data coverage (any verified autario indicator across World Bank, FRED, Eurostat, OECD, IMF, WHO, ECB, US Census, SEC). It omits any caveat about minimum series length or frequency requirements.

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?

Front-loaded with the model formula and the core purpose, then supporting context. The pipe-separated clauses are slightly clipped and the source list is long, but every element is on-topic and no sentence is filler.

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?

With no output schema, the description must describe returns, and it does ('per-timepoint components + summary amplitude'). Combined with the annotation coverage and full schema descriptions, an agent has enough to call it correctly; only edge-case behavior (short series, non-seasonal data) is unaddressed.

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 description coverage is 100%, so period, time, entity, indicator and full are all documented in the schema itself. The description only adds a general statement about indicator eligibility ('any verified autario indicator'), which the indicator parameter description already covers via list_indicators/get_entity_profile. Baseline 3 applies when the schema does the heavy lifting.

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?

Names a specific statistical technique and its additive model (Y = trend + seasonal + residual) and states the concrete outcome: strip the seasonal cycle to reveal the underlying trend. This is clearly distinguishable from siblings like decompose_drivers, find_drivers, rolling_stats, and correlate without opening any 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?

Gives a clear usage context ('great for monthly or quarterly data (retail sales, unemployment)') and the analytical goal, which is more than most siblings offer. It does not, however, state when NOT to use it or name an alternative tool (e.g. trend-only smoothing vs decompose_drivers), so the routing guidance is incomplete.

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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