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get_dataset_info

Read-onlyIdempotent

Get full metadata for a specific dataset including title, description, publisher (World Bank, FRED, Eurostat, OECD, WHO, IMF, ECB, US Census, SEC, or your own connector), category, keywords, row count, creation date, AND ontology fields (topic, subtopic, unit, frequency, entity_type, indicator_id, source_time_col, source_value_col, source_entity_col, data_granularity). The unit field carries the canonical measurement label (e.g. "Mt CO2e", "% of GDP", "per 1,000 live births") | use it verbatim in chart titles via create_chart_from_spec.title. Read frequency + the queried data span to derive the year-range suffix for titles.

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 retrieve metadata 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

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, and non-destructive behavior, so no contradiction exists. The description adds valuable behavioral context beyond annotations by defining the semantic meaning of the unit field and directing agents to use it verbatim in chart titles and to derive year-range suffixes from frequency plus data span.

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 a single dense paragraph with the core purpose front-loaded, followed by a compact field enumeration and two actionable usage notes. Every sentence earns its place, and 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?

There is no output schema, so the description carries the full burden of explaining the return shape. It lists the complete metadata fields and adds non-obvious guidance about unit and frequency usage. Combined with well-documented parameters and safety annotations, the agent has enough information to invoke and use the tool correctly.

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?

The input schema already covers both parameters at 100%, so the baseline is 3. The description does not add extra detail about dataset_id or format, though it does clarify the nature of the returned metadata, which indirectly helps the agent understand what the dataset_id refers to.

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 action ('Get full metadata') and a specific resource ('a specific dataset'), then enumerates exactly which metadata fields are returned. This clearly distinguishes it from siblings like get_dataset_schema and query_dataset by emphasizing metadata and ontology fields rather than schema or data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It clearly implies this is the tool to call when you have a dataset_id and need full metadata, and it adds downstream guidance for using the unit and frequency fields. However, it never explicitly contrasts this tool with sibling tools like search_datasets or get_dataset_schema, so there is no direct when-not-to-use guidance.

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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