Autario Data Analytics Platform

get_entity_data

Read-onlyIdempotent

Fetch data for ONE entity across MULTIPLE indicators — joined automatically on time via shadow columns. This is the "cross-dataset join" capability: no manual relationship setup needed. BY DEFAULT returns a pre-computed indicator.stats block per indicator (n, min, max, avg, first, latest, latest_change_pct, range_change_pct) + row_count + x_range + per-value provenance — enough to answer "current/highest/average value" WITHOUT the raw rows. Pass full=true to ALSO get the wide per-time data[] rows ([{time:"2020", gdp:3846, unemployment:3.8, …}], heavy). Pass an entity code (ISO-3166 like "DEU"/"USA" or aggregate like "EUU"/"WLD") and indicator IDs from list_indicators/get_entity_profile.

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, e.g. "2010-2023" or "2020". Format: YYYY or YYYY-YYYY
`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.
`entity_id`	Yes	Entity code (e.g. "DEU", "USA", "EUU")
`indicators`	Yes	Indicator IDs (max 10). Get these from list_indicators or get_entity_profile.

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already declare safe read-only behavior. Description adds context about automatic joining, the stats block contents, and token implications of full=true, enhancing transparency beyond annotations.

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?

Front-loaded with key purpose and capability. Uses bullet-like structure but could be slightly more concise. Still efficient and well-organized.

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?

Covers all critical aspects: purpose, inputs, output structure, default vs. full mode, and references to sibling tools. No output schema, but description compensates fully.

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%, but description enriches parameters by detailing the stats block fields, explaining ISO-3166 entity codes, and illustrating the structure of the full data rows.

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 it fetches data for one entity across multiple indicators with automatic time-based joining, and distinguishes itself from get_entity_profile and compare_entities by highlighting its cross-dataset join capability.

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?

Provides explicit guidance on when to use the default summary vs. the full raw data, and how to obtain entity codes and indicator IDs from sibling tools. Does not explicitly state when not to use, but 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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