generate_summary_report

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

With no annotations, the description must disclose behavioral traits. It mentions the return format (JSON with Markdown content) and implies it is a read-only generation (no mention of writes or side effects). However, it does not state whether the tool triggers any external actions, whether it is idempotent, or whether it requires special permissions. The absence of such details leaves some gaps, but the return format disclosure is a positive addition.

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 concise, structured with Args and Returns sections, and front-loads the core purpose. It avoids redundancy by not repeating every schema detail, but it does include the necessary parameter specifics inline. Each section earns its place, and the length is appropriate.

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?

The description covers the parameters and return format, and there is an output schema that likely specifies the exact fields. However, given the large set of sibling tools, it lacks context on when to use this tool vs. others, and it does not mention any prerequisites or side effects. The description is adequate for calling the tool but not for understanding its role in the workflow.

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 zero property descriptions (coverage 0%), so the description carries the full burden of explaining parameters. It explicitly defines report_type as daily/weekly, and for date_range it provides a required object format with example, and warns that integers are not allowed. This adds meaningful constraints beyond the plain schema, enabling correct usage.

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 generates a daily/weekly hotspot summary report (自动生成热点摘要报告). It identifies the resource (summary report) and the action (generate), and specifies the report frequency. However, it does not explicitly differentiate from siblings like aggregate_news or analyze_topic_trend, so it stops short of full differentiation.

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 is provided on when to use this tool versus the many sibling tools. It does not mention alternatives, conditions, or exclusions. The description simply states what the tool does without positioning it within the larger toolbox, so an agent would lack context for selection.

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