get_institution_metrics

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 explains the tool returns time series data, lists available categories, and describes item_query behavior with examples. It does not mention destructive actions, authentication, or rate limits, but since it is a read-only query tool, the description sufficiently conveys the non-destructive nature.

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 well-structured, front-loading the main purpose and using bullet points for categories. It is informative but slightly lengthy due to detailed examples and Korean text. Every sentence adds value, though some redundancy could be trimmed.

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 the tool has 5 parameters and no output schema, the description covers the primary use case, explains categories and item_query, and provides an alternative for staff summary. It does not describe the output format or pagination, but for a metrics query tool, the essential context is present.

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 coverage is 0%, so the description must add meaning. It explains category values and item_query usage with concrete examples, but does not fully describe org_code, year_from, or year_to. The defaults for year_from and year_to (0) are not explained, leaving ambiguity about their meaning. The description partially compensates for the lack of schema descriptions but has gaps.

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 main purpose: '기관의 공시지표 시계열을 조회합니다.' (query institution disclosure indicator time series). It lists specific categories and explains how item_query works, distinguishing it from the sibling tool get_institution_staff_summary by recommending the latter for a summary of personnel status.

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 provides explicit guidance on when to use this tool (time series of disclosure metrics) and recommends an alternative (get_institution_staff_summary) for staff summaries. It explains categories and item_query usage, but does not explicitly state when not to use it relative to other siblings like list_metric_categories or list_metric_items.

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