Autario Data Analytics Platform

what_matters

Read-onlyIdempotent

HEADLINE OP: given an outcome metric + entity, rank which other metrics best explain the outcome. Auto-selects candidates from the ontology if candidates is omitted (same topic + entity_type). Returns a ranking with confidence labels (strong/suggestive/weak/inconclusive) + reason strings + sharpen-suggestions pointing at related domains not yet included. Frequencies are auto-aligned to the coarser common grain — no inflated n-counts. Use this instead of find_drivers when you want a narrative-grade answer.

Input Schema

TableJSON Schema

Name	Required	Description
`time`	No
`entity`	Yes	Entity code (e.g. USA, DEU)
`outcome`	Yes	Indicator id of the outcome metric
`candidates`	No	Optional comma-separated candidate indicator ids. If omitted, auto-selects from ontology.

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable behavioral details: auto-selection from ontology, confidence labels, sharpen-suggestions, frequency alignment, and no inflated n-counts. 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.

Conciseness5/5

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

The description is two sentences, front-loading the core purpose, then detailing key behaviors. Every clause adds information without redundancy or irrelevant detail. Punctuation and structure support readability.

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 explains return format (ranking with confidence labels, reason strings, sharpen-suggestions) and important nuances (frequency alignment, no inflated n-counts). It covers all key aspects for an agent to use the tool effectively.

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?

Schema coverage is 75% (three of four parameters have descriptions in schema). The description adds meaning for 'candidates' (auto-selection behavior) but does not elaborate on 'time' or further clarify 'entity'/'outcome' beyond schema. This adds value but is not exhaustive.

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 states the tool ranks which other metrics best explain a given outcome metric and entity, using specific verbs ('rank') and resources ('metrics'). It explicitly distinguishes from the sibling tool 'find_drivers' by noting this tool is for 'narrative-grade' answers.

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?

The description provides clear guidance: 'Use this instead of find_drivers when you want a narrative-grade answer.' It also explains auto-selection behavior when 'candidates' is omitted, helping the agent decide when to rely on default behavior versus manual candidates.

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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