data_go_kr_query

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

With no annotations, the description carries full burden but provides minimal behavioral insight. It mentions the data source and scope but lacks details on authentication needs, rate limits, error handling, response format, or pagination behavior (implied by page/per_page params). This is inadequate for a query tool with multiple parameters.

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, efficient sentence that front-loads the core purpose. It could be slightly more structured by separating scope examples, but there's no wasted text.

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 query tool with 4 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain how to interpret results, handle errors, or navigate the data portal's service structure, leaving significant gaps for an AI agent to use it effectively.

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%, providing baseline documentation for all 4 parameters. The description adds no additional parameter semantics beyond implying service_id corresponds to data domains listed, but doesn't explain how to find valid service IDs or format params object. Baseline 3 is appropriate given high schema coverage.

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 action ('Query') and resource ('Korean government open data from data.go.kr'), with examples of data domains (real estate, weather, etc.). It distinguishes from siblings by specifying the Korean government data source, but doesn't explicitly contrast with similar query tools like finnhub_query or semantic_scholar_search.

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?

No guidance on when to use this tool versus alternatives is provided. The description mentions data domains but doesn't specify prerequisites, constraints, or compare with sibling tools that might handle similar data types (e.g., for transportation vs. tmap_route).

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