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get_entity_data

Read-onlyIdempotent

Fetch data for ONE entity across MULTIPLE indicators, joined automatically on time via shadow columns, even when the indicators come from different publishers (World Bank GDP next to FRED unemployment next to Eurostat energy). This is the "cross-dataset join" capability: no manual relationship setup needed. BY DEFAULT returns a pre-computed indicator.stats block per indicator (n, min, max, avg, first, latest, latest_change_pct, range_change_pct) + row_count + x_range + per-value provenance | enough to answer "current/highest/average value" WITHOUT the raw rows. Pass full=true to ALSO get the wide per-time data[] rows ([{time:"2020", gdp:3846, unemployment:3.8, …}], heavy). Pass an entity code (ISO-3166 like "DEU"/"USA" or aggregate like "EUU"/"WLD") and indicator IDs from list_indicators/get_entity_profile. 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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fullNoReturn the full raw time series (heavy, many tokens). Default false → you get only the summary/stats, which is enough to ANSWER a question. Set true only when you must plot or export every point.
timeNoOptional time range, e.g. "2010-2023" or "2020". Format: YYYY or YYYY-YYYY
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.
entity_idYesEntity code (e.g. "DEU", "USA", "EUU")
indicatorsYesIndicator IDs (max 10). Get these from list_indicators or get_entity_profile.

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. Changed1 schema field changed
    • addedInput schema / properties / full
      Added value: +{
+  "description": "Return the full raw time series (heavy, many tokens). Default false → you get only the summary/stats, which is enough to ANSWER a question. Set true only when you must plot or export every point.",
+  "type": "boolean"
+}
  4. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already establish the safety profile (readOnlyHint=true, idempotentHint=true, destructiveHint=false), and the description adds substantial behavior beyond that: the default response is a pre-computed indicator.stats block rather than raw rows, full=true switches to heavy wide data[] rows, and the join occurs automatically via shadow columns. The concrete token economics (17,000 raw rows down to roughly 200 tokens) is unusually honest about cost behavior.

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 core capability is front-loaded in the first sentence and the description follows a logical flow: capability, default output, full option, parameter sourcing, then cost rationale. It is dense but organized; the closing TOKEN PRECISION paragraph partially repeats the heaviness warning from the full=true sentence, though the concrete 17,000-to-200-token comparison keeps it informative rather than redundant.

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 the return shape — and it delivers: it names every indicator.stats field (n, min, max, avg, first, latest, latest_change_pct, range_change_pct), row_count, x_range, per-value provenance, and even gives an example wide row ({time:'2020', gdp:3846, unemployment:3.8}). Combined with prerequisites, conditional behavior, and cost guidance, nothing an agent needs to call this tool correctly is missing.

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%, setting the baseline at 3, and the schema's own parameter descriptions are already strong (especially for full). The description adds real value on top: entity_id is enriched with the ISO-3166-vs-aggregate taxonomy ('EUU'/'WLD' beyond the schema's 'DEU'/'USA'), and the token-precision guidance ties the time and full parameters to concrete cost consequences.

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: 'Fetch data for ONE entity across MULTIPLE indicators, joined automatically on time via shadow columns.' It scopes the capability further ('even when the indicators come from different publishers') and brands it as the 'cross-dataset join' capability with no manual relationship setup needed, which clearly separates it from siblings like query_dataset or compare_entities. An agent can tell exactly what this tool is for without opening the schema.

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?

Provides clear decision context: use the default summary output to answer 'current/highest/average value' questions, pass full=true only for plotting/export, and source entity/indicator IDs from list_indicators/get_entity_profile. The capped 'ONE entity' emphasis and cross-dataset-join framing imply exclusions, but no alternative tool is named for the when-not-to-use cases (e.g., multi-entity comparison via compare_entities), so it stops short of explicit routing.

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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