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

With no annotations, the description carries full behavioral burden. It discloses the main return components (description, AI synthesis, stance distribution, related videos), but does not mention side effects, permissions, or error behavior. Since it's a get_tool, read-only nature is implied but not stated.

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, compact sentence with clear structure, front-loading 'Topic 상세' and using separators to list contents. Every word adds value with no redundancy.

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 simple 1-parameter schema and no output schema, the description does a good job of outlining the expected response content. However, it omits parameter semantics and usage conditions, leaving some gaps for an agent relying solely on the description.

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?

The schema has one undocumented string parameter 'id' with 0% description coverage. The description does not explicitly explain that 'id' refers to a topic identifier, forcing the agent to infer it from the tool name and context. The description fails to compensate for the schema gap.

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 this tool returns detailed topic information (설명, AI 합성, stance 분포, 관련 영상), distinguishing it from siblings like list_topics and get_entity. The verb 'get' is in the name, and the description specifies the resource and scope.

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?

Usage is implied: this is for retrieving detailed topic data. However, there is no explicit guidance on when to use this tool versus alternatives like list_topics or get_macro_snapshot, and no exclusions are provided.

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