Autario Data Analytics Platform

get_entity_profile

Read-onlyIdempotent

Get the indicators available for one entity (country, aggregate, etc.). Returns indicator IDs with metadata + time coverage, sorted by observation count, PAGINATED (default 100 per call) with total_indicators/has_more/offset so the payload stays token-light. Page with offset, or narrow with topic. Use this to discover what you can query about Germany, USA, G7, or any known entity. Entity IDs are ISO 3166 codes (DEU, USA, CHN) or World Bank aggregates (WLD, EUU, EMU, SSF).

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Max indicators to return (default 100, max 500)
`topic`	No	Optional: filter indicators by topic
`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.
`offset`	No	Pagination offset (default 0). When has_more is true, pass offset = previous offset + returned for the next page.
`entity_id`	Yes	Entity code (e.g. "DEU" for Germany, "USA" for United States, "EUU" for European Union, "WLD" for World)

TDQS

A4.4/5.0

Behavior4/5

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

Annotations indicate readOnlyHint, idempotentHint, destructiveHint. The description adds pagination details (default 100, offset, has_more), topic filtering, and token-light format, which are not captured by annotations. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

The description is a single paragraph that front-loads the core action and includes essential details (pagination, filtering, examples). It is concise but covers necessary context without verbosity.

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?

Though no output schema is provided, the description thoroughly explains the return structure (indicator IDs, metadata, time coverage, total_indicators, has_more, offset). Combined with parameter details and usage examples, it is contextually complete for this tool.

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 100% with descriptions for all 5 parameters. The description supplements by explaining pagination mechanics, default limit, and entity code formats (ISO 3166, World Bank aggregates), adding value beyond schema.

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 retrieves indicators for an entity (country, aggregate) with metadata and time coverage. It distinguishes from siblings like get_entity_data by focusing on discovery of what indicators are available.

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 advises 'Use this to discover what you can query about Germany, USA, G7, or any known entity.' It provides entity code examples and mentions filtering by topic. While it doesn't explicitly say when not to use, the context is clear.

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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