get_statisticsHeadline figures computed live from the atlas: totals by medium and by kind of place, the top countries, the busiest and most famous locations, and — the number worth quoting — how many productions Wikidata records only at country level and therefore cannot place. The source for any aggregate claim about this dataset.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that figures are 'computed live' (implying real-time calculation) and includes specific details about the data source (productions recorded only at country level). It doesn't mention performance, caching, or error behaviors, but for a read-only statistics tool, the provided details are sufficient.
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, well-structured sentence that front-loads the key function ('Headline figures computed live from the atlas') and then provides detail. No fluff; each clause conveys meaningful 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?
Given no parameters and no output schema, the description fully explains what the tool returns and the context of use. It's complete for an agent to decide when to call it and what to expect. The tool's complexity is low, so the description is adequate.
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, which simplifies semantics. The schema description coverage is 100% (no params), so the description doesn't need to explain parameter meanings but it effectively tells the agent what the tool returns (the aggregate figures). This adds value beyond the empty schema.
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 the tool computes headline figures from the atlas, including specific aggregates (totals by medium, kind of place, top countries, busiest/most famous locations, and the number of productions only recorded at country level). The verb 'computed live' and resource 'atlas' provide a specific purpose, and it distinguishes itself from sibling tools that likely return lists or locations rather than aggregate statistics.
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 aggregate claims about the dataset are needed, stating 'The source for any aggregate claim about this dataset.' It doesn't explicitly contrast with siblings, but the focus on aggregate counts and the explicit 'cannot place' statistic clarifies a specific use case. However, it lacks explicit when-not-to-use instructions.
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 answers a distinct query type: aggregate statistics, country counts, nearby places, ranked places, name searches, production-to-places, and place-to-productions. The two full-question tools are clearly explicit inverses, so an agent should rarely select the wrong one.
The set mixes multiple patterns: get_/list_/search_ verb prefixes, noun-based names like top_locations and locations_near, and full question phrases like what_was_filmed_here. Each name is descriptive, but there is no single consistent verb_noun convention across the surface.
Eight tools is well-scoped for a read-only atlas: core lookup, inverse lookup, name search, geospatial search, ranking, country aggregates, and headline statistics. No tool feels redundant or missing at the same level of detail.
The domain is covered comprehensively: production-to-places, place-to-productions, location search, nearby search, ranking, country counts, and dataset-level statistics. For a Wikidata-backed read-only atlas there are no obvious dead ends; finer-grained scene data is explicitly out of scope.