groupsList all thematic groups (topic categories) in the data.gov.au CKAN catalogue with names and slugs; useful for discovering available subject-area filters.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No |
| Name | Required | Description | Default |
|---|---|---|---|
| count | Yes | Number of items returned. | |
| items | Yes | List of themes (groups/categories) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "limit": 50
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "count": {
+ "description": "Number of items returned.",
+ "type": "integer"
+ },
+ "items": {
+ "description": "List of themes (groups/categories)",
+ "items": {
+ "properties": {
+ "description": {
+ "description": "Group description",
+ "type": "string"
+ },
+ "id": {
+ "description": "Group UUID",
+ "type": "string"
+ },
+ "image_url": {
+ "description": "Group image URL",
+ "type": "string"
+ },
+ "name": {
+ "description": "Group name/slug",
+ "type": "string"
+ },
+ "package_count": {
+ "description": "Number of packages in group",
+ "type": "number"
+ },
+ "title": {
+ "description": "Group display title",
+ "type": "string"
+ }
+ },
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "required": [
+ "items",
+ "count"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds useful context about the resource and output fields (names and slugs), but does not detail behavior like pagination or response structure. With annotations covering safety, this is adequate but not rich.
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 action ('List all') and includes the resource, output fields, and a practical use case. No unnecessary words or repetition.
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 presence of an output schema and strong annotations, the description covers the core purpose and output contents. However, it omits an explanation of the 'limit' parameter and does not reference sibling tools like 'tags' or 'organizations' for alternative facets, leaving minor gaps.
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?
The only parameter, 'limit', is not explained in the description, and the schema has 0% description coverage. The description does not compensate by clarifying how the limit affects results, leaving the agent without explicit guidance on this parameter.
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 a specific verb ('List') and resource ('thematic groups in the data.gov.au CKAN catalogue'), and specifies the output (names and slugs). It also distinguishes from sibling tools like 'tags' and 'organizations' by describing groups as topic categories and subject-area filters.
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 provides clear context on when to use the tool ('useful for discovering available subject-area filters'), but it does not explicitly mention alternatives or exclude cases. This aligns with 'clear context, no exclusions'.
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 detailed descriptions and distinct purposes, but there is overlap among the question-answering tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and among the six Polymarket-related tools. An agent could reasonably misselect when choosing between the pipeworx variants or between entity_profile and recent_changes.
Tool names consistently use snake_case and most follow a verb_noun or noun pattern, with clear domain prefixes like pipeworx_ and polymarket_. Minor deviations include single-word verbs (search, subscribe) and adjective_noun names (recent_alerts, deep_research), but the overall pattern is predictable.
With 37 tools, the server is well beyond the 25+ threshold, and the bulk of them are unrelated to the 'Datagov Au' name. The actual data.gov.au catalogue is served by just six tools (search, package, resource, organizations, groups, tags), while the rest are Pipeworx, Polymarket, memory, subscription, and utility tools — a grossly bloated and miscategorized surface.
For a server named Datagov Au, the CKAN tools only cover read-only catalogue lookups; there are no tools for data.gov.au-specific updates, queries, or subscriptions. Conversely, the Pipeworx router exposes access to 5,502 internal tools, but many of those are not directly callable as MCP tools, leaving a confusing gap where discover_tools returns tools the agent cannot actually invoke.