Autario Data Analytics Platform

compare_entities

Read-onlyIdempotent

Compare ONE indicator across MULTIPLE entities (e.g. GDP of DEU vs USA vs CHN). BY DEFAULT returns a per-entity summary (first/latest/min/max/avg/count) — enough to say who is highest and how current levels compare — plus row_count + x_range. Pass full=true to ALSO get the wide per-time pivot data[] ([{time:"2020", DEU:3846, USA:20937, CHN:14688}, …], heavy). Use this for country comparisons, cross-region analyses, or any chart that compares the same metric across entities.

Input Schema

TableJSON Schema

Name	Required	Description
`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" or "2020"
`format`	No	Output wire format for this MCP call. Default 'toon' (Token-Oriented Notation, fewest tokens, best for tabular rows). 'compact' = minified JSON. 'json' = pretty JSON for readability. The REST API always returns JSON regardless.
`entities`	Yes	Entity codes to compare (max 50). E.g. ["DEU","USA","CHN"]
`indicator`	Yes	Indicator ID to compare. Get from list_indicators.

TDQS

A4.7/5.0

Behavior5/5

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

Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint), the description details the default behavior (returns per-entity summary) and the optional full mode (returns pivot data), noting that full is heavy. This adds significant behavioral context that helps the agent understand output format and token usage.

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 concise at three sentences, front-loaded with purpose, and uses formatting (e.g., 'BY DEFAULT', 'full=true') for emphasis. Every sentence earns its place without redundancy.

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

Completeness5/5

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

Given no output schema, the description adequately covers what to expect: a per-entity summary with stats and row_count/x_range, and optionally a pivot dataset. It includes use-case examples and covers all key aspects, making it complete for the tool's complexity.

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

Parameters4/5

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

With 100% schema coverage, the baseline is 3. The description adds value by explaining that 'full=true' returns heavy pivot data and that the default summary is sufficient for answering questions. This enriches the understanding beyond the schema descriptions.

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 explicitly states it compares one indicator across multiple entities, using a concrete example (GDP of DEU vs USA vs CHN). It clearly identifies the verb 'compare' and the resource 'entities', and distinguishes its use case from siblings by specifying it's for cross-region analyses and chart comparisons of the same metric.

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 directly states when to use the tool: 'Use this for country comparisons, cross-region analyses, or any chart that compares the same metric across entities.' While it does not explicitly list when not to use or name specific alternatives, the guidance is clear and actionable.

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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