factiq

Search datasets

search_datasets

Read-only

Find datasets by keyword across every schema — the first discovery step.

Keyword ranking (not semantic) over dataset titles and topics. Returns ranked {schema, dataset_code, title, topic}. Follow up with describe_dataset for detail, or run_sql on the series/dimensions tables for exact series. latest_data_date, when present, is the period start date of the newest stored value in the whole dataset (left out when unknown); one part of a dataset can stop earlier, so check the end dates of the series you use.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "search_datasets"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No
`query`	Yes
`schemas`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 16, 2026

TDQS

A4/5.0

Behavior4/5

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

Annotations already cover the safety profile (readOnlyHint, destructiveHint=false), so the bar is lower, and the description still adds real behavior: keyword rather than semantic ranking, the returned fields, and the important caveat that `latest_data_date` is the dataset-wide newest period and that individual parts can end earlier. That caveat is non-obvious and prevents misuse.

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-loaded with the purpose and the discovery-step framing, then follows with ranking behavior, return shape, next steps, and a caveat. Every sentence carries information; the `latest_data_date` aside is the only slightly tangential element but it is genuinely non-obvious and useful.

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?

An output schema exists, so return values need not be fully explained, and the description still sketches the ranked result shape. Combined with ranking behavior, follow-up routing, and the date caveat, an agent has enough to call this correctly; only the parameter semantics remain under-specified.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and the description does not document any of the three parameters. 'Across every schema' loosely gestures at the `schemas` filter, but `limit` and the actual semantics of `schemas` (default null = all schemas) are left entirely to the schema, which lacks descriptions — a significant gap for a search tool where result-set control matters.

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 gives a specific verb and resource ('Find datasets by keyword across every schema') plus a clear scope statement, and it frames itself as 'the first discovery step', which separates it from sibling search tools like search_series or describe_dataset. The ranking mechanism (keyword, not semantic) further pins down what kind of search this is.

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 explicitly states the workflow position ('first discovery step') and names concrete follow-ups (`describe_dataset` for detail, `run_sql` on series/dimension tables for exact values). What's missing is an explicit contrast with the closest alternative, `search_series` — the agent must infer datasets-vs-series itself.

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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