Autario Data Analytics Platform

rolling_stats

Read-onlyIdempotent

Rolling window statistics (mean/std/min/max/sum) for an indicator. Smooths noise, reveals trends.

Input Schema

TableJSON Schema

Name	Required	Description
`op`	No	mean \| std \| min \| max \| sum
`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
`window`	No	Window size in periods (2-100)
`indicator`	Yes

TDQS

A3.5/5.0

Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint as safe. The description adds interpretive context (smoothing, trends) but no additional behavioral traits like authentication needs or rate limits. The description does not contradict 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?

The description is a single 12-word sentence with no redundant words. It efficiently conveys the core functionality and purpose without unnecessary detail.

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

Completeness2/5

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

With no output schema and 6 parameters, the description lacks critical context about the return format or how to interpret results. The param 'full' in schema mentions returning summary vs. full series, but the description omits this guidance. The agent would need to rely heavily on the schema and parameter descriptions for proper usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 50%, with three parameters (op, full, window) described in the schema. The main description does not add any param-specific information beyond listing the operations in the op field, which is already covered. The description fails to compensate for the uncovered parameters (entity, indicator, time).

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 clearly states the tool computes rolling window statistics (mean, std, min, max, sum) for an indicator, with the purpose of smoothing noise and revealing trends. It distinguishes from sibling statistical tools like lag_analysis or pct_change by specifying the rolling window and smoothing aspect.

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

Usage Guidelines3/5

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

The description implies use cases (smoothing noise, revealing trends) but does not explicitly state when to use this tool versus alternatives like lag_analysis or pct_change. No direct comparison or exclusion criteria are provided, making guidelines implicit rather than explicit.

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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TDQS

A3.9/5.0

Disambiguation4/5

Most tools are strongly domain-specific with clear boundaries, especially the 360 reports and dataset/chart CRUD tools. Some overlap exists around driver analysis (find_drivers, what_matters, decompose_drivers) and dataset discovery (search_datasets, discover_by_topic, list_indicators), but the descriptions make the intended use cases mostly distinguishable.

Naming Consistency4/5

The vast majority of tools follow a clear snake_case verb_noun or get_noun pattern, e.g. list_connectors, refresh_connector, query_dataset, delete_dataset. Minor deviations such as calculate, describe, bubble_or_not, what_matters, and the 360-style report names keep it from being perfectly uniform.

Tool Count2/5

48 tools is far beyond the 3-15 range and even beyond the 25-tool threshold for a heavy surface. The platform is broad and the tools are organized into domains, but the sheer number creates a high selection burden for an agent and suggests the server is trying to cover too many workflows in one toolset.

Completeness4/5

The toolset covers dataset lifecycle, chart lifecycle, data discovery, querying, statistics, app context, connectors, and admin reports remarkably well. Notable gaps are the lack of a delete_chart tool and no row-level update/delete for datasets, but agents can generally work around these or treat them as intentional platform constraints.

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