Skip to main content
Glama

Autario

by com.autario
OverviewDiscussionsAdmin

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), or a table another account shared with you (read-only, listed by get_my_workspace under shared_with_you) | 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

A4.7/5.0
Behavior5/5

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

Annotations already carry the safe-read profile (readOnlyHint/idempotentHint/destructiveHint=false), and the description still adds real behavioral context: aggregates are numerically correct despite text storage (no lexicographic min/max), the engine 'refuses to combine columns of different kinds', null-heavy datasets warrant non_null_only, and the token-cost contrast (~17,000 raw rows vs ~200 tokens). That is information an agent cannot get from the annotations or schema.

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-loads scope, then alternatives, then token strategy — good ordering, and the pipe-delimited opening keeps the enumeration compact. It is on the long side with some restatement (the mostly-null dataset point appears twice, and the aggregation rationale is repeated). Every sentence still does work, so it lands just short of 5.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an 11-parameter, mutation-free analytical tool with no output schema, the description covers return shape (rows plus per-category statistics, or summary only), the safety-relevant combination restriction, and the required citation. Minor gap: it claims returns 'as JSON' while the format parameter defaults to 'toon', a small internal inconsistency an agent must reconcile on its own.

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 description coverage is 100%, so the baseline is 3; the description nonetheless adds meaning by explaining *why* and *when* to use summary_only (finished per-column stats with NO raw rows) and non_null_only (drop empty rows), and by framing aggregate/groupby as the token-efficient path with a worked example. It does not restate sort/filter/fields syntax, which the schema already covers well.

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?

Opens with a specific verb+resource ('Query the rows of ONE dataset'), enumerates the data universe it covers (World Bank/FRED/Eurostat/... plus uploads and shared tables), and explicitly carves out the sibling it is not ('Reach for get_entity_data instead when you want ONE entity across SEVERAL indicators'). An agent can select this over query-adjacent siblings without opening either 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?

Gives explicit routing rules ('Prefer aggregations or summary_only over pulling raw rows'), a concrete worked example ('average GDP of Germany 2010-2020 => aggregate=avg(value) + filters'), named alternatives (get_entity_data, get_dataset_schema), and a precondition ('Call get_dataset_schema first and read its semantics block'). When-to-use, when-not-to-use and prerequisites are all present.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Autario toolsInspect
Try in Browser
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources