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by com.autario
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regression

Read-onlyIdempotent

Linear regression of y ~ x for one entity. Returns slope, intercept, R² and interpretation. Use for "how does X predict Y?" questions. Runs on any verified autario indicator (World Bank, FRED, Eurostat, OECD, IMF, WHO, ECB, US Census, SEC).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
xYesIndependent variable (predictor) indicator ID
yYesDependent variable (target) indicator ID
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.
timeNo
entityYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. 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"
+}
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds useful behavioral context beyond annotations by stating the return payload (slope, intercept, R², interpretation) and the supported data sources (World Bank, FRED, Eurostat, etc.), which helps agents know what to expect.

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 tight sentences convey the method, output, use case, and data source scope without any filler. The most decision-relevant information is front-loaded, and every sentence earns its place.

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, moderately complex statistical tool with no output schema, the description covers the essentials: what it computes, what it returns, the supported universe of inputs, and when to use it. The only notable omission is detail on the optional 'time' parameter, but the core invocation path is well specified.

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 60%, with descriptions for x, y, and full but not for time or entity. The description clarifies the roles of x and y via the formula 'y ~ x' and implies entity is a single country/region, but it does not explain the 'time' parameter at all, leaving a clear 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 names a specific statistical method ('Linear regression of y ~ x'), the resource ('one entity'), and the concrete outputs (slope, intercept, R², interpretation). It also differentiates from sibling tools like correlate by emphasizing prediction ('how does X predict Y?') and the single-entity scope, making it clear when this tool is the right choice.

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 explicitly states when to use the tool: 'Use for "how does X predict Y?" questions.' This gives clear context for invocation, though it does not explicitly mention when not to use it or name alternative tools for correlation or lag-based questions.

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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