Autario Data Analytics Platform

list_indicators

Read-onlyIdempotent

Browse the Autario indicator registry — semantic layer over all 2600+ datasets. Each indicator has a topic (economy, health, energy, …), unit (USD, %, years, …), frequency (year/month/day), and entity_type (country/subnational/aggregate). Use this to discover what data is available before querying it. Much more precise than search_datasets when you know what topic or unit you need.

Input Schema

TableJSON Schema

Name	Required	Description
`unit`	No	Filter by unit: USD \| EUR \| % \| per capita \| per 1000 \| years \| tonnes \| tonnes CO2 \| GWh \| TWh \| index \| count \| …
`limit`	No	Max results (default 50, max 500)
`topic`	No	Filter by topic: economy \| finance \| trade \| marketing \| health \| demographics \| education \| energy \| environment \| food \| technology \| media \| housing \| transport \| tourism \| space \| government \| military \| minerals
`search`	No	Full-text search across indicator titles + descriptions
`frequency`	No	Filter by frequency: year \| quarter \| month \| week \| day
`publisher`	No	Filter by publisher (World Bank, Eurostat, FRED, WHO, …)
`entity_type`	No	Filter by entity_type: country \| subnational \| aggregate \| company \| security

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds context about the semantic layer and filter facets, but no behavioral contradictions. It slightly exceeds annotation coverage.

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?

Two sentences: first defines the tool and its facets, second gives usage guidance and comparison. Every sentence adds value, no wasted words.

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?

For a read-only filter tool with 7 optional parameters and no output schema, the description sufficiently conveys what the tool does, how to use it, and why it's useful. No missing critical details.

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 has 100% coverage with detailed descriptions for each parameter. The description lists available facets but does not add significant per-parameter detail beyond the schema. Baseline 3 is appropriate.

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 it's for browsing 'the Autario indicator registry — semantic layer over all 2600+ datasets' with specific facets (topic, unit, frequency, entity_type). It clearly distinguishes from sibling 'search_datasets' by claiming precision advantage.

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?

Explicitly says 'Use this to discover what data is available before querying it' and provides a direct comparison: 'Much more precise than search_datasets when you know what topic or unit you need.'

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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