Autario

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

Name	Required	Description
`full`	No	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.
`time`	No
`entity`	Yes
`period`	No	Seasonal period in time steps (12=monthly, 4=quarterly, 7=weekly). Auto-inferred from indicator frequency if omitted.
`indicator`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedJun 28, 2026

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"
+}

First observedMay 6, 2026

TDQS

A4/5.0

Behavior4/5

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

Annotations already mark the tool as read-only, idempotent, and non-destructive, so the description does not need to repeat that. It adds useful behavioral context by specifying the additive model type, that it returns per-timepoint components plus summary amplitude, and that it runs on verified autario indicators, which goes beyond what annotations provide.

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 compact and front-loaded with the key formula and purpose. The parenthetical list of data sources adds somewhat more detail than strictly necessary, but it is still a short, readable description with no wasted filler.

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

Completeness3/5

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

The description covers the model, use case, return values, and supported data sources, which is solid for a read-only analytical tool. However, with no output schema and two required parameters undocumented in both the schema and description, there are meaningful gaps around how to specify the input series and interpret the returned components.

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 coverage is only 40%, and the description partially compensates by explaining what 'indicator' accepts (verified autario data sources) and what kind of output the user gets. However, required parameters like 'entity' and the optional 'time' parameter are left undocumented in both the schema and description, so the description does not fully make up for the coverage gap.

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 mathematical operation ('Additive decomposition Y = trend + seasonal + residual') and a concrete use case ('strip the seasonal cycle... reveal the underlying trend'). This clearly distinguishes it from sibling tools like lag_analysis or rolling_stats, and the examples (retail sales, unemployment) further anchor what it does.

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 says 'Use this to strip the seasonal cycle' and notes it is 'great for monthly or quarterly data', giving clear when-to-use context. It does not explicitly mention when not to use it or name alternatives, but the seasonal focus makes the appropriate context reasonably clear.

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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