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The most filmed places

top_locations

The atlas ranked, worldwide or inside one country or one kind of place. Order is fame rank, which comes from the most widely covered production shot there — so rank 1 is the place the most famous production used, not the place with the most credits. Sort by productions instead to get the busiest places (Los Angeles, New York, Vancouver). Answers "most famous filming locations in ".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNoRanking (default fame)
limitNoHow many (default 10)
countryNoCountry name or ISO code (optional — omit for worldwide)
categoryNoKind of place — key or slug: studio (Studios & sets), castle (Castles & palaces), landmark (Landmarks & buildings), street (Streets & squares), nature (Landscapes & nature), city (Towns & cities), region (Countries & regions) (optional)

TDQS

A4.4/5.0
Behavior4/5

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 rank is based on the most widely covered production shot, not total credits, and explains sorting by 'productions' yields busiest locations. This adds meaningful behavioral nuance beyond the schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with purpose and key distinction, no fluff. Every clause adds value, making it highly efficient for agent parsing.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has only optional parameters and no output schema. The description explains ordering semantics and typical queries, which is sufficient for selecting and invoking correctly. It doesn't detail return format, but that's not required given the complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, giving baseline 3. The description adds value by explaining the semantic difference between 'fame' and 'productions' sorting and clarifies that rank 1 is the most famous production used, not the most credits. It also implies country/category usage via the example question.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool ranks places by fame or productions, worldwide or filtered by country/category. It explicitly differentiates from siblings like search_locations (search vs ranking) and locations_near (proximity), using specific verbs and scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description says it answers 'most famous filming locations in <country>' and instructs to sort by productions for busiest places, providing clear usage context. It lacks explicit when-not-to-use guidance for alternatives, but the context is strong enough for an agent to decide.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.3/5.0
Disambiguation5/5

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.

Naming Consistency3/5

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.

Tool Count5/5

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.

Completeness5/5

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.