get_catalog
Full service catalog with complete param specs, return fields, prices, and cache TTLs. Free.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Full service catalog with complete param specs, return fields, prices, and cache TTLs. Free.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that the tool is free and that it returns full catalog data, but it does not disclose whether it has side effects, requires authentication, has rate limits, or how the data is returned. The lack of explicit read-only or safety context leaves important gaps.
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 compact sentence that front-loads the core purpose and lists the catalog's key contents. The additional 'Free.' fragment is short and communicates a relevant fact without unnecessary verbosity.
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?
For a zero-parameter tool with no output schema, the description does well by outlining exactly what the catalog contains: param specs, return fields, prices, and cache TTLs. It doesn't provide details on response format or potential size, but those are secondary for such a simple, self-describing read operation.
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 tool has zero parameters, so there is nothing for the description to document beyond the schema. Since the schema has 100% coverage for its empty properties and the description doesn't need to explain parameters, the baseline of 4 applies.
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 identifies the tool as returning the full service catalog and specifies the contained information (param specs, return fields, prices, cache TTLs), making the purpose evident. It does not explicitly differentiate from the sibling list_services, so it misses some opportunity to distinguish itself.
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 when a complete catalog of service details is needed, but it gives no explicit guidance on when to use this tool versus list_services or other alternatives. There is no when-not-to-use guidance or mention of how this tool relates to sibling tools.
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.
Every tool maps to a clearly distinct dataset or lookup, with country prefixes and topic names separating overlapping domains. Even similar tools like au-abs-building-activity and au-abs-building-approvals are unambiguously differentiated by their descriptions.
The data tools follow a consistent country/topic hyphenated pattern (au-*, nz-*), making resource selection predictable. The meta tools (get_catalog, list_services, health) break this pattern with imperative/underscore names, but this is a minor and understandable deviation.
At 26 tools, the set is on the heavy side and slightly exceeds the typical comfortable range. However, each tool represents a genuinely distinct data service, and the clear grouping by country and topic keeps the surface navigable.
The server covers a broad range of common agent data needs for Australia and New Zealand: demographics, income, building, labour, weather, time, holidays, school terms, and place resolution. Minor gaps exist, such as no NZ building data or broader international coverage, but core workflows are well supported.