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

Annotations already cover readOnlyHint=true and openWorldHint=false, so the description adds real value beyond them: it discloses the 30-day/article eligibility filter that can make the call return nothing, explains that kind/line are AI-written only when listed in ai_written (otherwise the repository's own GitHub description), and notes the license is only present when the article leans on one. It does not discuss error behavior for unknown repositories.

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 return-value inventory is front-loaded and specific, but the text is long and the trailing English sentence largely restates the preceding Korean clause, producing redundancy rather than added information. The parenthetical figure list also bloats the opening sentence.

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 an output schema present, the description need not explain return shapes, yet it still enumerates the fields and — crucially — the eligibility restriction that governs whether a call returns data at all. Combined with one fully documented parameter and safety annotations, an agent has enough to invoke it correctly.

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?

There is a single parameter at 100% schema description coverage, and the schema already gives the format (owner/name, e.g. vllm-project/vllm) and accepts a GitHub URL. The description adds no additional meaning about the name argument, so the baseline of 3 applies.

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 states concretely what the tool returns: one GitHub repository's page on trend.ondo.sh, with its figures, kind/line, date, figures_read_on, address and optional license. That is far more specific than the bare name 'repository', and it is clearly distinguished by scope from the keyword and today siblings (single repository vs. keyword lists vs. daily view), though it never names those siblings.

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 only implied: the eligibility rule ('only repositories that trended on GitHub in the last 30 days or have an article are answered') tells the agent when the call will succeed, which is useful. But nothing says when to pick this over keyword or today, and no prerequisites or fallback behavior are stated.

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