聚合中国新闻

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

Annotations already mark the tool as read-only and open-world, and the description adds useful behavioral context: it forms clusters based on title, keyword, and time proximity, and it promises not to generate facts that cannot be traced. This extra grounding/hallucination-avoidance guarantee is valuable beyond the annotations. It does not mention pagination or response shape, but the annotation coverage lowers the burden.

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 Chinese sentence with no redundant filler. It front-loads the primary behavior before adding the traceability guarantee, and every part contributes either to purpose or to an important behavioral constraint.

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 tool has seven optional parameters, no output schema, and no parameter-level documentation, yet the description gives only a high-level behavior summary. It does not state what the returned event clusters look like, how limits and sorting affect output, what date range defaults apply, or how source filtering behaves. For an agent to invoke this tool correctly, significantly more contextual detail is needed.

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 0%, and the description provides no meaningful parameter-level guidance. It only implies that some query text is involved and that time/cluster criteria matter, but it does not explain how 'from', 'to', 'sort', 'limit', 'category', 'source_ids', or 'query' should be used. With seven parameters and no schema descriptions, the description fails to compensate for the missing parameter semantics.

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 action: query news and group it into event clusters using similar titles, keywords, and temporal proximity. This distinguishes it from sibling search tools that retrieve individual articles rather than aggregating them into clusters. Even without naming a sibling, the clustering mechanism makes the tool's purpose concrete.

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 context is implied rather than explicit. The clustering language suggests this tool is appropriate when an agent needs grouped event coverage rather than a flat search result list, and 'China' in the name suggests geographic scope. However, it never explicitly says when to prefer aggregate_china_news over search_china_news or aggregate_international_news, and no exclusions or alternative routing are provided.

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