Autario Data Analytics Platform

discover_by_topic

Read-onlyIdempotent

Discover the most relevant verified datasets for a given topic. Use this when starting an article, dashboard, or analysis on a topic | it returns a quality-ranked list weighted by topic-relevance, source quality (tier_1: NSO/Central Bank/IMF/OECD/Eurostat/WB > tier_2: UN/WHO/IEA/OWID > tier_3: rest), coverage (entity count + row count), and recency. Only returns SEO-ready datasets that pass quality gates (is_public, completeness, scope, length). Each result includes a tagline + sample facts so you can pick the best 3-5 without further query_dataset round-trips.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No	Max datasets to return (1-50, default 10).
`topic`	Yes	The topic to find datasets for. Free-form, matches against asset topic field, title, keywords, category, and enriched description. Examples: "AI investment", "EU energy transition", "global inflation", "tech platform shifts"
`depth_pref`	No	Preferred dataset shape. "timeseries" for trend articles (daily/weekly/monthly/quarterly/yearly cadence), "cross-sectional" for snapshots (rankings, lists), "any" for no preference.	any
`recency_window`	No	Filter by data freshness. Default "any" returns all datasets regardless of last_refreshed_at; tighter windows for time-sensitive articles.	any

TDQS

A4.5/5.0

Behavior4/5

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

Description adds value beyond readOnlyHint/idempotentHint by detailing ranking tiers (tier_1, tier_2, tier_3), quality gates (is_public, completeness, scope, length), and that results are SEO-ready. It doesn't mention auth or side effects, but annotations already cover safety.

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 with four sentences, front-loading the purpose and use case. Every sentence provides unique value without repetition, making it efficient and easy to parse.

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?

Despite no output schema, the description explains that results include tagline and sample facts, enabling the agent to pick top datasets without extra calls. It covers parameter behavior, ranking logic, and quality gates, fully addressing 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?

All parameters have schema descriptions (100% coverage). The description adds context on how parameters affect ranking (topic-relevance weight, recency_window filtering) and explains the output includes tagline+sample facts, which aids selection.

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 discovers relevant verified datasets by topic, using a specific verb and resource. It distinguishes from siblings like query_dataset and search_datasets by emphasizing quality-ranked results and tagline+sample facts to avoid round-trips.

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?

The description explicitly says to use when starting an article, dashboard, or analysis, and that results include sample facts to avoid further query_dataset calls. It implies not to use for deep queries but lacks explicit when-not-to-use language.

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.

Resources

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