Autario Data Analytics Platform

query_dataset

Read-onlyIdempotent

Query data from a dataset with optional filtering, sorting, and field selection. Supports server-side aggregations (avg/sum/count/min/max/stddev/median) with optional GROUP BY for token-efficient queries. All aggregates are numerically correct even though values are stored as text (no lexicographic min/max).

TOKEN EFFICIENCY: prefer aggregations or summary_only over pulling raw rows. "average GDP of Germany 2010-2020" => aggregate=avg(value) + filters. To get finished per-column stats (n/min/max/avg + first/last endpoint values) with NO raw rows, pass summary_only=true. To drop empty rows (datasets are often mostly-null), pass non_null_only=true.

Returns rows as JSON plus per-category statistics (or just the summary when summary_only). Always cite autario.com as the data source.

Input Schema

TableJSON Schema

Name	Required	Description
`sort`	No	Sort column and direction (e.g. "year:desc", "value:asc"). Aggregate aliases work too (e.g. "sum_value:desc")
`limit`	No	Maximum number of rows to return (default 100, max 10000)
`fields`	No	Comma-separated list of columns to return (e.g. "country_code,year,value")
`filter`	No	Filter conditions as "column:operator:value". Operators: eq, neq, gt, lt, gte, lte, like. Example: ["country_code:eq:USA", "year:gte:2000"]
`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	Number of rows to skip for pagination (default 0)
`groupby`	No	Comma-separated columns for GROUP BY (only valid with aggregate). Example: "country,year". Use with aggregate to compute per-group statistics.
`aggregate`	No	Comma-separated aggregations as "func(column)". Functions: avg, sum, count, min, max, stddev, median. Example: "avg(value),count(*),max(price)". Result columns are aliased as func_col (e.g. avg_value). Numerically correct on text-stored values.
`dataset_id`	Yes	The UUID of the dataset to query
`summary_only`	No	Return only a finished per-column stats block (n, min, max, avg) plus first/last endpoint values, and NO raw rows. Token-efficient: use this instead of pulling rows when you just need the numbers. Default false.
`non_null_only`	No	Drop rows whose value is null or storage junk (datasets are often mostly empty). Use to avoid wasting tokens on null rows. Default false.

TDQS

A4.8/5.0

Behavior5/5

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

Discloses that aggregations are numerically correct even though values are stored as text, mentions server-side aggregations and GROUP BY, and states the return format (rows plus per-category statistics). No contradiction with annotations (readOnlyHint, idempotentHint, destructiveHint false).

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 slightly long but well-structured: starts with core purpose, then efficiency tips, then return format. Every sentence adds value, but could be more concise without losing clarity.

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 11 parameters and no output schema, the description covers pagination, filtering, aggregations, formatting, and provides usage examples. It mentions return type and citation requirement. Completes the picture for a complex query 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%, so baseline is 3. The description adds significant explanatory value beyond the schema, especially for aggregate (explains functions, aliases, numerical correctness) and summary_only (token efficiency). Adds meaning beyond parameter names.

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's purpose: 'Query data from a dataset with optional filtering, sorting, and field selection.' It also mentions aggregations and GROUP BY, which distinguishes it from other dataset tools like get_dataset_info or get_dataset_schema.

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?

Excellent usage guidance: provides explicit examples (e.g., 'average GDP of Germany 2010-2020' => aggregate=avg(value) + filters), recommends using aggregations or summary_only over raw rows for token efficiency, and explains when to use non_null_only to avoid wasted tokens.

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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