list_datasetsPaginated catalog of ONS datasets. Returns id, title, description, contacts, release frequency, last release, theme.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | 1-100 (default 20) | |
| offset | No | 0-based offset (default 0) |
| Name | Required | Description | Default |
|---|---|---|---|
| total | Yes | Total number of datasets available | |
| datasets | Yes | List of normalized datasets | |
| returned | Yes | Number of datasets returned in this page |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "limit": 20,
+ "offset": 0
+ },
+ {
+ "limit": 50,
+ "offset": 20
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "datasets": {
+ "description": "List of normalized datasets",
+ "items": {
+ "properties": {
+ "contacts": {
+ "description": "Contact information",
+ "items": {
+ "properties": {
+ "email": {
+ "description": "Contact email",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "name": {
+ "description": "Contact name",
+ "type": [
+ "string",
+ "null"
+ ]
+ }
+ },
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "description": {
+ "description": "Dataset description",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "id": {
+ "description": "Dataset ID",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "latest_version_id": {
+ "description": "ID of the latest version",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "license": {
+ "description": "License information",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "national_statistic": {
+ "description": "Whether it is a national statistic",
+ "type": [
+ "boolean",
+ "null"
+ ]
+ },
+ "next_release": {
+ "description": "Next release date",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "publisher": {
+ "description": "Publisher name",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "release_frequency": {
+ "description": "Release frequency",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "state": {
+ "description": "Dataset state",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "theme": {
+ "description": "Theme category",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "title": {
+ "description": "Dataset title",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "type": {
+ "description": "Dataset type",
+ "type": [
+ "string",
+ "null"
+ ]
+ }
+ },
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "returned": {
+ "description": "Number of datasets returned in this page",
+ "type": "integer"
+ },
+ "total": {
+ "description": "Total number of datasets available",
+ "type": [
+ "integer",
+ "null"
+ ]
+ }
+ },
+ "required": [
+ "total",
+ "returned",
+ "datasets"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the agent knows it's a safe, non-destructive read operation. The description adds that the tool is paginated, which is consistent and useful context beyond 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?
The description is a single sentence that front-loads the key feature (paginated catalog) and lists return fields succinctly. Every word serves a purpose with no redundancy.
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?
Given the tool's simplicity (2 optional params, output schema exists), the description fully covers purpose, return format, and pagination. No gaps remain for an agent to invoke 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 both 'limit' and 'offset' having descriptions. The description does not add extra meaning beyond what is in the schema; it only mentions 'paginated' which aligns with the parameters. Baseline score of 3 is appropriate.
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 it is a paginated catalog of ONS datasets and lists the specific fields returned (id, title, description, contacts, release frequency, last release, theme). It distinguishes from siblings like 'get_dataset' by emphasizing the list and pagination.
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 implies use for browsing datasets via pagination, but does not explicitly state when to use this tool versus alternatives like 'get_dataset' or 'list_editions'. No when-not-to-use or alternative guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly separated jobs, but the set is crowded with overlapping research/query entry points: ask_pipeworx and ask_pipeworx_beta are described as currently identical, and the Polymarket/research cluster has fuzzy boundaries. Detailed descriptions help, but an agent can still easily mis-select among these tools.
Names are consistently snake_case and readable, but they mix verb-led commands (get_dataset, list_editions, validate_claim) with noun-led descriptive names (entity_profile, polymarket_edges, recent_changes) and a version-suffixed duplicate (ask_pipeworx_beta). The ONS tools also lack a shared ons_ prefix aside from ons_timeseries, so the naming is more a collection of conventions than one predictable pattern.
37 tools is well past the heavy range for a server whose name suggests a focused UK ONS statistics surface; only about six tools actually serve ONS data, while the rest span memory, subscriptions, prediction markets, npm scanning, AI visibility, and general Pipeworx plumbing. The count is not an extreme 50+ sprawl, but it is too many for a coherent scope.
The core ONS read workflow is well covered: catalog discovery through list_datasets, dataset/edition/version metadata, filtered get_observations, and classic time series via ons_timeseries. Minor gaps exist, such as no dedicated dataset search and some peripheral one-off features like scan_dependency or generate_llms_txt, but agents can work around them.