공공데이터 조합 추천

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

Beyond the readOnly and idempotent annotations, the description adds important behavioral context: it operates on a static list and does not actually fetch, process, or combine data; those steps are left to the user after checking official pages. This boundary-setting is valuable.

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 two sentences, front-loaded with the core action and followed by a scope clarification. Every sentence carries necessary information with no redundancies.

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 read-only recommendation tool, it covers the main workflow and output (data types, institutions, links), but lacks details on optional parameters and result ranking/format. Given the absence of an output schema, more could be added, but it is largely sufficient.

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?

With 0% schema description coverage, the description only partially compensates. It explains that the goal is broken into data needs, which relates to 'goal' and 'data_needs', but it does not describe 'limit' or 'regions' parameters or their formats.

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 decomposes a service/analysis goal into data needs and recommends relevant public data types, institutions, and official verification links from the entire static list. This specific verb+resource+scope distinguishes it from sibling tools like search_public_datasets or list_public_data_sources.

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?

It provides a clear usage context: when the user has a desired service/analysis and wants to identify relevant public data. However, it does not explicitly mention alternatives or when not to use this tool, which would make it a 5.

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