list_editionsList all named editions of an ONS dataset (e.g., "time-series", "annual"). Returns each edition label, release date, state, and the latest version ID to use with get_version or get_observations.
| Name | Required | Description | Default |
|---|---|---|---|
| dataset_id | Yes | ONS dataset ID |
| Name | Required | Description | Default |
|---|---|---|---|
| total | Yes | Total number of editions | |
| editions | Yes | List of editions | |
| dataset_id | Yes | Dataset ID |
Changes observed during successful MCP inspections.
Input schema / examplesAdded value: +[
+ {
+ "dataset_id": "cpih01"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "dataset_id": {
+ "description": "Dataset ID",
+ "type": "string"
+ },
+ "editions": {
+ "description": "List of editions",
+ "items": {
+ "properties": {
+ "edition": {
+ "description": "Edition label",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "latest_version_id": {
+ "description": "ID of the latest version for this edition",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "release_date": {
+ "description": "Release date",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "state": {
+ "description": "Edition state",
+ "type": [
+ "string",
+ "null"
+ ]
+ }
+ },
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "total": {
+ "description": "Total number of editions",
+ "type": "integer"
+ }
+ },
+ "required": [
+ "dataset_id",
+ "total",
+ "editions"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds value by detailing the specific return fields (edition label, release date, state, latest version ID), which provides behavioral context beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the core purpose, and no unnecessary words. Every sentence adds value: first states the action and scope, second describes return values and usage.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (1 parameter, list operation) and has an output schema. The description mentions all key return fields and provides downstream guidance, making it fully complete for an agent to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with dataset_id described as 'ONS dataset ID'. The description does not add further details about the parameter; it only mentions the dataset in the context of listing editions. Baseline 3 is appropriate as schema already covers it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists all named editions of an ONS dataset, with specific examples ('time-series', 'annual'). It distinguishes from siblings by mentioning downstream tools like get_version and get_observations, providing context for its role.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says it returns data to use with get_version or get_observations, indicating when to use it. It does not provide explicit when-not guidance, but for a list tool with one parameter, the usage is clear from context.
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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