Create CityJSON subset

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description bears the full responsibility for behavioral disclosure. It says a new dataset_id is returned, which is useful, but it does not explain whether the original dataset is modified, whether subsetting is deterministic, whether method combos are allowed, or what side effects or prerequisites exist. For a transformation tool, this leaves important behavior unknown.

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 concise: two sentences with no filler. The main action and output are front-loaded, and the list of subsetting methods is easy to parse. It earns its place, though it sacrifices enough detail to cap the score below 5.

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 seven parameters, no output schema, no annotations, and low schema coverage, the description is too thin to fully enable correct invocation. It does not explain required dataset_id provenance, valid combinations of subsetting criteria, coordinate/reference constraints beyond bbox, or the exact meaning of 'exclude'. An agent would likely need trial and error or additional external knowledge.

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 description coverage is only 29%, so the description must compensate for undocumented parameters. It does clarify that 'random' means random count and 'types' refers to CityObject types, and it names bbox, radius, and IDs. However, it never addresses the 'exclude' parameter, does not explain how criteria combine, and leaves parameter formatting/units to inference.

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 creates a derived CityJSON subset and enumerates the selection methods: IDs, bbox, radius, random count, and CityObject types. It goes beyond the title by specifying the tool (cjio) and the output (new dataset_id), but does not explicitly differentiate it from sibling tools like cityjson_filter_lod or cityjson_query, which may also produce subsets.

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 usage for subsetting CityJSON data by several common criteria, giving an agent a reasonable sense of when to invoke it. However, it provides no explicit guidance about when not to use it or which sibling tool might be more appropriate for related tasks such as LOD filtering, querying, or exporting.

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