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rolling_stats

Read-onlyIdempotent

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 (default: mean)
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.
windowNoWindow size in periods (2-100, default 5)
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. Changed5 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)."
    • changedInput schema / properties / op / description
      Previous value: -"mean | std | min | max | sum"New value: +"mean | std | min | max | sum (default: mean)"
    • 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."
    • changedInput schema / properties / window / description
      Previous value: -"Window size in periods (2-100)"New value: +"Window size in periods (2-100, default 5)"
  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

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds that it smooths noise and reveals trends, and lists supported data providers, but does not disclose return format, rate limits, or authentication needs beyond what annotations and schema 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 front-loaded with the core operation and then adds usage guidance and data coverage. It is appropriately sized, though the provider list in the final sentence is somewhat lengthy and could be trimmed without losing much.

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?

Given the rich schema (100% coverage) and no output schema, the description supplies enough purpose and usage context to select and invoke the tool. It could be more complete by mentioning when not to use it versus statistical siblings, but the core gaps are minor.

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 all six parameters already have detailed documentation, including op, full, time, entity, window, and indicator. The description repeats the op values and indicator sources but adds no syntax or semantic meaning beyond the schema, making the baseline 3 appropriate.

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 states a specific computational operation and resource: rolling window statistics (mean/std/min/max/sum) for an indicator. It does not explicitly differentiate itself from statistical siblings such as pct_change, lag_analysis, or correlate, so it falls short of a 5.

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 use case: use before claiming a turning point in a monthly FRED or Eurostat series. It also notes the tool runs on any verified autario indicator. However, it does not explicitly name when to use alternatives or when not to use this tool.

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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