get_seoul_dataset_detail

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It specifies what the tool returns (provider, department, update cycle, last updated date, SRV_TYPE, and a swaggerUrl), and transparently discloses a key limitation: it cannot resolve individual API endpoint details. This is meaningful behavioral context 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.

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

The description is compact and front-loaded: it states the core action first, then lists returned fields, and ends with a necessary caveat. The limitation note earns its place because it prevents the agent from attempting to use this tool to retrieve API endpoint specifications. No wasted words.

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?

The tool is simple, with one well-documented parameter and no output schema. The description compensates by naming the key return values and the swaggerUrl limitation. It could be slightly more explicit about the full response shape, but for a single-dataset metadata lookup, the provided information is sufficient for correct invocation.

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 100%, and the schema already explains that detailUrl is a dataset detail page URL or service ID with concrete examples. The description adds context about what the tool does with that parameter, but no additional semantic depth beyond the schema. A baseline score of 3 is appropriate.

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 retrieves detailed metadata for a specific Seoul dataset, naming the specific returned fields (provider institution, department, update cycle, last updated date, format). This clearly differentiates it from sibling tools like search_seoul_datasets or list_seoul_recent_updates, which operate over collections rather than a single dataset detail.

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 the tool is used when you have a specific detailUrl or service ID, since that is the required parameter. It also explicitly states what the tool cannot do—retrieve individual API request URLs and parameter specs—and directs the user to the returned swaggerUrl's 'Open API' tab as the alternative. It does not explicitly name sibling alternatives for finding datasets, but the usage context is clear.

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