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referencesource.org

by io.github.joelp22-maker
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List reference datasets

list_datasets
Read-only

What reference datasets are available? Lists every published dataset with its title, record count, last-verified date and a one-sentence summary. Covers regulatory thresholds, version compatibility, certification registers, licensing rules and more. Pass query to list only the datasets matching a topic — that is much cheaper than reading the whole list. Use the returned slug with search_records or get_record.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fullNoReturn each dataset's complete scope statement instead of the first sentence. Off by default: the full text is roughly four times the size and is rarely needed to choose a dataset.
queryNoOptional topic filter, e.g. "boiler", "tariff" or "loan limit". Case-insensitive substring, matched against each dataset's slug, title and full description; if that phrase matches nothing, the words are matched separately and the answer says so in `matched_on`. Omit it to list every dataset.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / query / description
      Previous value: -"Optional topic filter, e.g. \"boiler\", \"tariff\" or \"loan limit\". Case-insensitive substring, matched against each dataset's slug, title and full description. Omit it to list every dataset."New value: +"Optional topic filter, e.g. \"boiler\", \"tariff\" or \"loan limit\". Case-insensitive substring, matched against each dataset's slug, title and full description; if that phrase matches nothing, the words are matched separately and the answer says so in `matched_on`. Omit it to list every dataset."
  2. Changed2 schema fields changed
    • addedInput schema / properties / full
      Added value: +{
+  "description": "Return each dataset's complete scope statement instead of the first sentence. Off by default: the full text is roughly four times the size and is rarely needed to choose a dataset.",
+  "type": "boolean"
+}
    • addedInput schema / properties / query
      Added value: +{
+  "description": "Optional topic filter, e.g. \"boiler\", \"tariff\" or \"loan limit\". Case-insensitive substring, matched against each dataset's slug, title and full description. Omit it to list every dataset.",
+  "type": "string"
+}
  3. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, non-destructive and closed-world, so the safety profile is covered. The description adds meaningful behavioral context beyond that: a cost/size tradeoff for the unfiltered list, the ~4x size of full scope text, and the degraded-match fallback surfaced in `matched_on`. It stops short of describing pagination, limits, or the result envelope, which keeps it out of the top band.

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?

Three sentences, the payload (what it returns, how to filter cheaply, what to do with the slug) is front-loaded, and the opening question is answered immediately rather than left hanging. No sentence is filler.

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 correctly enumerates the returned fields (title, record count, last-verified date, one-sentence summary) and the dataset domains covered. Combined with annotations and a 100%-documented 2-parameter schema, an agent has everything needed to call it and use the result.

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 description coverage is 100%, so both `full` and `query` are already fully documented in the schema, including the substring semantics and matched_on fallback. The description only restates the `query` use case and adds a cost cue; it says nothing about `full`, which the schema has to carry alone. Baseline 3 is the correct ceiling here.

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?

States a specific verb and resource ('lists every published dataset') plus the exact fields returned (title, record count, last-verified date, summary). It also names where to go next (search_records / get_record), so an agent can distinguish it from its siblings without opening any schema.

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?

Explicitly says to pass `query` for topic-filtered listing, when to omit it ('Omit it to list every dataset' is in the schema, and the description frames the whole-list case), and warns that the filtered path is 'much cheaper than reading the whole list.' It also states the follow-up workflow: use the returned slug with search_records or get_record.

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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