Search StackData's catalog of 50k+ official statistical datasets (SDMX/World Bank dataflows, China censuses and yearbooks, tariff schedules). Multilingual: an English query finds Chinese-titled tables. Returns dataset ids usable with the other tools.
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | search query | |
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral burden. It discloses meaningful behaviors: multilingual search (English query finds Chinese-titled tables), the scope of the catalog, and that output is dataset IDs. However, it does not mention pagination, ordering, default limit behavior, or whether the search is fuzzy/prefix-based, leaving some behavioral ambiguity.
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 compact sentences deliver the essential information: what is searched, the catalog size, content types, multilingual behavior, and the output type. It is front-loaded, specific, and contains no filler or repeated schema information.
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 simple two-parameter search tool with no output schema, the description covers the catalog scope, search behavior, return value (dataset IDs), and downstream usage. Minor gaps like result count or paging are already partially covered by the schema defaults, so the description is reasonably complete.
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 only 50%, since 'limit' lacks a description. The description adds value by clarifying that 'q' supports multilingual matching, which goes beyond 'search query.' However, it does not address the 'limit' parameter or explain how many results may be returned, despite the schema's default/max values already existing.
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 states a specific verb ('Search'), a precise resource ('StackData's catalog of 50k+ official statistical datasets'), and enumerates concrete content types. It makes clear the tool is a discovery/search layer whose outputs feed other tools, distinguishing it from siblings like get_table or tariff_lookup.
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 clearly implies when to use this tool: before the other dataset tools, since it 'returns dataset ids usable with the other tools.' It also highlights the multilingual matching behavior as an important usage consideration. It does not explicitly list exclusion conditions or name alternative tools, so not a full 5.
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.
Each tool targets a distinct operation: catalog search, exact table retrieval, natural-language data resolution, and tariff lookup. Even though search and resolve both accept English queries, one returns dataset IDs and the other returns observations, so the boundaries are clear.
Three tools follow a clear verb_noun pattern (get_table, resolve_data, search_datasets), and all names use lowercase snake_case. tariff_lookup deviates slightly from the verb-first pattern but remains consistent in style and readable.
Four tools is a well-scoped count for a read-only statistical data server. Each tool provides a distinct core capability with no redundant or filler tools.
The core read-only data workflow is covered: discover dataset IDs, fetch a known table, ask a data question, and run tariff lookups. Minor gaps such as schema-only metadata access or generic table filtering are not present, but agents can work around them with the existing tools.