Skip to main content
Glama
Autario

Autario

Official
by Autario
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Hybrid

rolling_stats

Read-onlyIdempotent

Compute rolling window mean, standard deviation, min, max, or sum for an Autario indicator to smooth noise and reveal trends before identifying turning points in monthly series.

Instructions

Rolling window statistics (mean/std/min/max/sum) for an indicator. Smooths noise, reveals trends | use it before claiming a turning point in a monthly FRED or Eurostat series. Runs on any verified autario indicator (World Bank, FRED, Eurostat, OECD, IMF, WHO, ECB, US Census, SEC).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
opNomean | std | min | max | sum
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
windowNoWindow size in periods (2-100)
indicatorYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv2.21.2
    • 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 observedv2.1.0

TDQS

A4/5.0
Behavior3/5

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

Annotations already disclose readOnly, idempotent, and non-destructive behavior, so the description's additive burden is lower. It adds useful context about the heavy full-output option and that default output is summary-only, but it doesn't mention window behavior at edges (e.g., how missing periods are handled) or the exact response shape. 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, front-loaded with what the tool does, followed by a clear usage directive and a scope qualifier. The 'Smooths noise, reveals trends | use it before...' phrase is slightly informal but information-dense and not redundant.

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?

Given a read-only, no-output-schema tool with 6 params and 50% coverage, the description states the use case, supported sources, and default summary behavior. It does not clarify the meaning of required 'entity' and 'indicator' or the 'time' param, so an agent may still be uncertain how to fill required fields correctly.

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 50%: the schema already documents op, full, and window, leaving entity, indicator, and time with only names. The description reinforces that op values are mean/std/min/max/sum and multi-sources, but it adds little detail beyond the schema for the undocumented params.

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?

States a specific verb and resource (rolling window statistics for an indicator) and lists the available operations (mean/std/min/max/sum). Distinguishes itself from siblings like pct_change, lag_analysis, and correlate by the windowing concept and its stated use case.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly tells the agent when to use it: before claiming a turning point in a monthly FRED or Eurostat series. Also names the data sources it runs on, which implicitly says when it applies, and the 'verified autario indicator' qualifier sets a usage condition.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools