Autario Data Analytics Platform

get_dataset_info

Read-onlyIdempotent

Get full metadata for a specific dataset including title, description, publisher, category, keywords, row count, creation date, AND ontology fields (topic, subtopic, unit, frequency, entity_type, indicator_id, source_time_col, source_value_col, source_entity_col, data_granularity). The unit field carries the canonical measurement label (e.g. "Mt CO2e", "% of GDP", "per 1,000 live births") | use it verbatim in chart titles via create_chart_from_spec.title. Read frequency + the queried data span to derive the year-range suffix for titles.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`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.
`dataset_id`	Yes	The UUID of the dataset to retrieve metadata for

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds behavioral context by specifying which fields are returned and how they should be used (e.g., unit for chart titles). This goes beyond the annotations, providing useful operational semantics without contradiction.

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 front-loaded with the main purpose and then details specific fields. It is two sentences, but the first sentence is long due to the field enumeration. It could be slightly more concise by omitting less critical fields, but overall it is well-structured and each part adds value.

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?

With no output schema, the description compensates by thoroughly listing returned fields and providing usage guidance for unit and frequency. This makes the tool's behavior clear despite the lack of a structured output definition. The description is complete for an informational retrieval tool.

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 description coverage is 100%, so the schema already documents both parameters. The description does not add additional meaning to the parameters themselves (dataset_id and format). It focuses on output fields, not input parameters. According to guidelines, when coverage is high, baseline is 3.

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 that the tool retrieves full metadata for a specific dataset, listing many fields including ontology fields. It distinguishes itself from siblings like search_datasets (which searches) and get_dataset_schema (which only returns schema). The verb 'get' and resource 'full metadata for a specific dataset' is specific and unambiguous.

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 provides explicit guidance on how to use the output: the 'unit' field should be used verbatim in chart titles via create_chart_from_spec.title, and 'frequency' plus data span should derive year-range suffix. It implies this tool is for retrieving metadata before chart creation. However, it does not explicitly state when not to use it or name alternatives, though context from siblings helps.

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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