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Autario

by com.autario
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search_datasets

Read-onlyIdempotent

Search the Autario data catalog by keyword | thousands of normalized public datasets from World Bank, FRED, Eurostat, OECD, WHO, IMF, ECB, US Census and SEC, plus your own uploads and connector tables (Google Search Console, GA4, Meta Ads, Google Ads, YouTube, TikTok, Instagram, Facebook, Shopify, LinkedIn, Bing). Returns dataset IDs, titles, descriptions, categories, publishers, row counts, last_refreshed_at, AND trusted ontology fields (topic, subtopic, unit, frequency, entity_type, indicator_id) when ontology confidence is high. Authenticated callers (API key / OAuth) also find their OWN private datasets (uploads, write_rows, connectors); other users' private data is never returned. Use this first to discover available datasets before querying. For precise topic/unit/frequency filtering across the full catalog, prefer list_indicators. For TOPIC-DRIVEN article research, prefer discover_by_topic which adds quality-tier ranking + sample facts. 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
pageNoPage number for pagination (default 1)
limitNoMaximum number of results to return (default 20, max 100)
queryNoSearch term to match against dataset titles, descriptions, and keywords (e.g. "GDP growth", "CO2 emissions", "unemployment rate")
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.
categoryNoFilter by category. Options: "Finance & Economics", "Trade", "Technology", "Health & Society", "Energy", "Environment", "Demographics", "Education", "Infrastructure"
visibilityNoWhich datasets to search: "public" catalog only, "private" only your own datasets, "both". Default: "both" when authenticated, "public" otherwise. Other users' private datasets are never returned.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / visibility
      Added value: +{
+  "description": "Which datasets to search: \"public\" catalog only, \"private\" only your own datasets, \"both\". Default: \"both\" when authenticated, \"public\" otherwise. Other users' private datasets are never returned.",
+  "enum": [
+    "public",
+    "private",
+    "both"
+  ],
+  "type": "string"
+}
  2. 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"
+]
  3. 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"
+}
  4. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the readOnly/destructive hints, the description discloses meaningful behavior: private datasets are only returned to authenticated owners, other users' private data is never returned, ontology fields are present only when confidence is high, and token-precision semantics are described. It also enumerates exact return fields, which matters because there is no output 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?

The description is long but dense and front-loaded: the core action appears first, followed by return fields, auth scoping, routing to alternatives, and a token-precision tip. The source and connector lists are extensive but earn their place by clarifying catalog scope; nothing feels 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?

For a search/discovery tool with no output schema, the description covers the needed ground: what the search matches, what fields are returned, visibility rules, precise alternatives, and token economy. Combined with the annotation safety profile and full parameter schema, an agent has enough to invoke it 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?

All six parameters already have rich descriptions in the input schema, covering 100% of schema fields, so the baseline is 3. The description reinforces the keyword concept and the auth-dependent visibility default, but it does not add material parameter details beyond 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?

The first clause states a specific verb, resource, and action: 'Search the Autario data catalog by keyword'. It also explicitly differentiates itself from sibling tools list_indicators and discover_by_topic, so an agent can select it confidently.

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?

The description says 'Use this first to discover available datasets before querying' and gives explicit routing rules: prefer list_indicators for precise topic/unit/frequency filtering and discover_by_topic for topic-driven article research. It also clarifies when private datasets are included, giving clear when-to-use and 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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