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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. The semantics block says what each column MEANS in one sentence (kind, definition, unit, currency, time grain, and where the meaning came from: the metric registry, the column name, the asset, or honestly none). autario refuses to combine columns of different kinds. Read the semantics field of the schema before combining two columns.

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

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. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already mark the call as read-only/idempotent, and the description adds substantive behavior beyond that: the shape/roles/cadence/cardinality/level_mix/ignore_cols/notes structure, the semantics-block provenance, and the constraint that 'autario refuses to combine columns of different kinds.' That turns a bare schema fetch into a predictable contract.

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 dense and front-loaded with purpose and usage constraints, but it is long and covers many nested datasheet fields inline. Most of the detail earns its place because there is no output schema, though it could be tightened by moving field enumerations into the schema or a shorter form.

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 carries the full burden of explaining what the agent will receive, and it delivers: datasheet subfields, enumerated shape values, role mapping, cadence, cardinality, level_mix, ignore_cols examples, semantics provenance, and the combining restriction. An agent can call this tool and interpret the result without guessing.

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%, so the schema already explains dataset_id and format, including the format enum and the toon/compact/json tradeoffs. The main description adds no parameter-level meaning beyond the overall return contract, so the baseline 3 applies.

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 opens with a specific verb and resource: 'Get the column names, data types, total row count, AND a machine-legible datasheet for a dataset.' It also frames the tool's role against siblings ('Always call this before query_dataset') and clarifies the single_series case, so an agent can distinguish it from get_dataset_info, query_dataset, and get_entity_data.

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?

It gives an explicit precondition: call before query_dataset and before charting, and states why (know columns / datasheet tells how to plot). It also names the alternative for single_series datasets ('query_dataset, not get_entity_data'), which is exactly the kind of routing guidance an agent needs.

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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