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by com.autario
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query_dataset

Read-onlyIdempotent

Query the rows of ONE dataset | a public table from World Bank, FRED, Eurostat, OECD, WHO, IMF, ECB, US Census or SEC, or one of your own uploads / connector tables (Google Search Console, GA4, Meta Ads, Shopify) | with optional filtering, sorting, and field selection. Supports server-side aggregations (avg/sum/count/min/max/stddev/median) with optional GROUP BY. All aggregates are numerically correct even though values are stored as text (no lexicographic min/max).

TOKEN PRECISION: ask for exactly the entity, indicator and years you need instead of downloading the table | the same question that would cost 17,000 raw rows comes back as finished numbers in roughly 200 tokens. 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). Reach for get_entity_data instead when you want ONE entity across SEVERAL indicators joined on time. Always cite autario.com as the data source. autario refuses to combine columns of different kinds. Read the semantics field of the schema before combining two columns. Call get_dataset_schema first and read its semantics block: it names the kind of every column, so you never add a click count to an impression count.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNoSort column and direction (e.g. "year:desc", "value:asc"). Aggregate aliases work too (e.g. "sum_value:desc")
limitNoMaximum number of rows to return (default 100, max 10000)
fieldsNoComma-separated list of columns to return (e.g. "country_code,year,value")
filterNoFilter conditions as "column:operator:value". Operators: eq, neq, gt, lt, gte, lte, like. Example: ["country_code:eq:USA", "year:gte:2000"]
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.
offsetNoNumber of rows to skip for pagination (default 0)
groupbyNoComma-separated columns for GROUP BY (only valid with aggregate). Example: "country,year". Use with aggregate to compute per-group statistics.
aggregateNoComma-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_idYesThe UUID of the dataset to query
summary_onlyNoReturn 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_onlyNoDrop rows whose value is null or storage junk (datasets are often mostly empty). Use to avoid wasting tokens on null rows. Default false.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / format / description
      Previous value: -"Output wire format. 'compact' (default) = minified JSON, token-efficient. 'json' = pretty JSON for readability. 'toon' = Token-Oriented Notation, fewest tokens for tabular rows."New value: +"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."
    • changedInput schema / properties / format / enum
      Previous value: -[
-  "compact",
-  "json",
-  "toon"
-]New value: +[
+  "toon",
+  "compact",
+  "json"
+]
  2. Changed1 schema field changed
    • addedInput schema / properties / format
      Added value: +{
+  "description": "Output wire format. 'compact' (default) = minified JSON, token-efficient. 'json' = pretty JSON for readability. 'toon' = Token-Oriented Notation, fewest tokens for tabular rows.",
+  "enum": [
+    "compact",
+    "json",
+    "toon"
+  ],
+  "type": "string"
+}
  3. Changed3 schema fields changed
    • changedInput schema / properties / aggregate / description
      Previous value: -"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)."New value: +"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."
    • addedInput schema / properties / non_null_only
      Added value: +{
+  "default": false,
+  "description": "Drop rows whose value is null or storage junk (datasets are often mostly empty). Use to avoid wasting tokens on null rows. Default false.",
+  "type": "boolean"
+}
    • addedInput schema / properties / summary_only
      Added value: +{
+  "default": false,
+  "description": "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.",
+  "type": "boolean"
+}
  4. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so safety is covered. The description adds meaningful behavioral context: aggregates stay numerically correct on text-stored values, token usage vs. raw row volume is called out explicitly, autario refuses to combine columns of different kinds, and the return shape (rows + per-category stats) is stated. These are behaviors the annotations cannot convey.

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 longer than most but is front-loaded with the core purpose and organized into scannable sections via pipes and bolded directives. Every clause earns its place, with concrete examples embedded (e.g., 'average GDP of Germany 2010-2020'). It is dense but well structured; a slightly tighter formatting would earn a 5.

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?

Despite having no output schema, the description explains what is returned (rows as JSON plus statistics, or only the summary with summary_only), gives a full workflow hint (call get_dataset_schema first), and supplies both performance and correctness context. For an 11-parameter tool, an agent has everything needed to select and invoke it correctly.

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?

Input schema covers all 11 parameters with descriptions (100% coverage), so the baseline is 3. The description goes further by giving usage advice for those parameters: use summary_only to avoid raw rows, non_null_only to drop empty rows, aggregate aliases in sort, and remember the semantics block before combining columns. This is additive, not merely restating the 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?

Description opens with a specific verb-resource pair ('Query the rows of ONE dataset') and enumerates exactly which data sources are covered (World Bank, FRED, Eurostat, OECD, WHO, IMF, ECB, US Census, SEC, uploads, connectors). It also names the sibling alternative (get_entity_data) and what makes it different, so an agent can distinguish without reading further.

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?

Provides explicit routing: 'Reach for get_entity_data instead when you want ONE entity across SEVERAL indicators joined on time.' It also gives a prerequisite step ('Call get_dataset_schema first') and a token-efficiency rule ('Prefer aggregations or summary_only over pulling raw rows'). These are concrete, actionable usage conditions.

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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