Autario

correlate

Read-onlyIdempotent

Compute Pearson + Spearman correlation between two indicators for one entity. Returns r, p-value, n, and human-readable interpretation. Use for "does X move with Y?" questions. Includes causation disclaimer automatically. Runs on any verified autario indicator (World Bank, FRED, Eurostat, OECD, IMF, WHO, ECB, US Census, SEC).

Input Schema

TableJSON Schema

Name	Required	Description
`a`	Yes	First indicator ID
`b`	Yes	Second indicator ID
`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	Optional time range: "2010-2023"
`entity`	Yes	Entity code (e.g. DEU)

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 declare readOnly, idempotent, and non-destructive behavior. The description adds meaningful context by listing return fields (r, p-value, n, interpretation) and disclosing that a causation disclaimer is automatically included. No contradiction with annotations.

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?

Three dense, information-rich sentences with no filler. The core operation and output are front-loaded, and every sentence contributes: what it computes, when to use it, and what data it runs on.

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 read-only statistical tool with fully documented parameters, the description covers the operation, output fields, intended question type, and supported data sources. There is no output schema, but the description summarizes return values sufficiently. Edge cases like short series or missing overlap are not mentioned but are not critical for correct invocation.

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 100%, so every parameter is already documented in the input schema. The description adds only light mapping context ('two indicators', 'one entity') and does not need to compensate further because the schema fully covers parameter meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Compute') and clearly names the resource: Pearson + Spearman correlation between two indicators for one entity. It also states what is returned. It does not explicitly name a sibling alternative like regression or lag_analysis, so it stops short of full differentiation.

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?

It gives a clear usage context: 'does X move with Y?' questions, and scopes valid inputs to verified indicators from named sources. However, it does not explicitly state when not to use this tool or mention alternatives such as regression or lag_analysis.

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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