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

Annotations already signal readOnlyHint=true, openWorldHint=true, and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context beyond annotations: it aggregates multiple evidence types, matches templates, outputs both filling guidance and a productionPrompt, and explicitly does not produce the finished script/copy/plan.

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 with no filler. The core purpose is front-loaded ('专家创作简报'), the aggregation scope is listed compactly, and the boundary condition about who completes the final draft is placed at the end without repetition.

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?

With no output schema, the description adequately explains what is returned: fill-in guidance and a productionPrompt. It also covers scope and boundary. It is slightly less complete on parameter interactions (e.g., platform vs template_id) and the exact format of the output, but for an agent deciding to call this tool, the essential information 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 description coverage is 100%, so the schema already documents all six parameters. The description indirectly maps to topic ('围绕主题'), platform ('同平台'), and template_id ('匹配模板'), but adds no semantic detail beyond what the schema provides, so the baseline 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 a specific verb+resource: it creates an expert brief by aggregating hot-topic evidence, search terms, sentiment signals, and viral samples, then produces fill-in guidance and a productionPrompt. It also distinguishes itself from content-generation tools by explicitly noting the final draft is done by the calling LLM.

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 gives clear context for when to use the tool: when you need a research-backed creative brief around a topic, with template matching and productionPrompt output. It implicitly excludes final-draft generation by saying that is the caller's responsibility, but it does not explicitly name alternatives or state when-not-to-use conditions.

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