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Autario Data Analytics Platform

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get_dataset_schema

Read-onlyIdempotent

Get the column names, data types, total row count, AND a machine-legible datasheet for a dataset. Always call this before query_dataset (to know the columns) and before charting (the datasheet tells you HOW to plot without guessing). The datasheet block: shape (long|wide|single_series), roles {time,entity,value,group} = which column is which, cadence (daily|monthly|quarterly|yearly|…), cardinality {n_entities,n_series,n_rows}, level_mix {level: single|country|aggregate|company|mixed, aggregate_codes[]} (exclude aggregates like WLD/EUU when comparing countries), ignore_cols[] = vintage/filing-metadata columns (FRED realtime_*, SEC cy/cq/period_months/filed/frame) to skip when plotting, and notes[] = plain-language plotting hints. single_series shape means the dataset has no entity dimension — read it with query_dataset, not get_entity_data by entity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatNoOutput 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_idYesThe UUID of the dataset to get the schema for

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already mark the tool as read-only and idempotent, but the description adds substantial behavioral context by explaining exactly what the `datasheet` contains, how to interpret `shape`, `roles`, `cadence`, `level_mix`, and `ignore_cols`, and how those affect downstream plotting. It also warns about excluding aggregate codes and legacy columns. No contradiction with annotations.

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 long but efficient: every sentence adds necessary operational detail. The main purpose is front-loaded, followed by direct usage guidance, then a well-structured breakdown of the `datasheet` fields. Despite its density, there is no filler or repetition.

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?

Given two parameters, no output schema, and rich annotations, the description fully compensates for the absence of an output schema by outlining exactly what the `datasheet` returns and how to interpret each field. It also covers the critical behavioral distinction for `single_series` datasets. Nothing essential seems missing for correct tool invocation.

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 coverage is 100%, with both `dataset_id` and `format` already described in the input schema. The description does not add new parameter-level semantics, but it references the returned schema structure enough to make the relationship between dataset and output clear. Baseline 3 is appropriate since the schema already carries parameter meaning.

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 identifies the action ('Get') and a concrete, specific resource: column names, data types, total row count, and a structured `datasheet`. It also differentiates the tool from siblings by explaining how it fits with `query_dataset` and `get_entity_data`, making its purpose unmistakable.

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?

The description gives explicit when-to-use guidance: always call before `query_dataset` and before charting. It also provides a conditional exclusion: for `single_series` datasets, read with `query_dataset`, not `get_entity_data`. This fully routes the agent to the correct tool.

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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