Ask a data question in English ('unemployment rate in Japan', 'compare GDP in Japan and Germany') — resolves to the best covering dataset and returns the observations, with coverage honesty (missing countries are reported, never papered over).
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | the question | |
| end | No | end year | |
| start | No | start year |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries the behavioral burden. It discloses that the tool returns observations and adds an important behavioral guarantee: missing countries are reported rather than silently omitted. It could mention failure when no dataset matches, but the disclosed behavior is meaningful and honest.
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, information-dense sentence. Examples are embedded in a parenthetical, and every clause contributes: the action, the resolution behavior, the return value, and a key honesty guarantee. No fluff 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?
For a 3-parameter tool with no output schema, the description covers the input style, resolution behavior, return content, and an important coverage guarantee. It does not detail output formatting or how failures are handled, but it provides enough context for an agent to invoke and interpret the result 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% for the three parameters, so the schema already documents start and end years. The description adds semantic value by clarifying that the q parameter is a natural-language English data question and provides concrete examples of valid input.
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 names a specific verb ('Ask') and resource ('data question') and explains that it 'resolves to the best covering dataset and returns the observations.' The natural-language query behavior clearly distinguishes it from sibling tools like get_table or search_datasets, which are more direct retrieval/search operations.
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 gives clear usage context: use this tool when you have a data question phrased in English, supported by examples like 'unemployment rate in Japan.' It does not explicitly name alternatives or exclusions, but the intended use case is unmistakable.
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.