aggregate_news

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

With no annotations provided, the description carries the full burden. It explains the core behavior (merging similar news from different platforms, showing coverage and heat) and describes the return format (JSON with dedup stats, aggregated list, platform coverage), which gives a clear picture of the tool's operation. It does not explicitly state whether it is read-only or mention potential side effects, but the aggregation nature and examples imply a safe query tool.

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 well-structured with a concise one-line summary, a brief overview paragraph, and clearly labeled Args, Returns, and Examples sections. It is slightly verbose with the Chinese text but every section earns its place, and the front-loaded summary aids quick understanding.

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 all parameters, describes return content, and provides two usage examples. It does not specify the exact format for date_range (schema allows string or object), nor does it list valid platform IDs, which could leave an agent unsure about valid inputs. However, given an output schema exists and the core behavior is described, it is fairly complete for an aggregation tool.

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?

Since schema description coverage is 0%, the description fully compensates by explaining each parameter beyond the schema. It provides meaning, defaults, constraints (e.g., similarity_threshold 0.3–1.0, higher = stricter), and platform list examples. This adds substantial value over the bare schema.

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 opens with a clear, specific purpose: '跨平台新闻聚合 - 对相似新闻进行去重合并' (cross-platform news aggregation with deduplication and merging). It further explains that the tool merges reports of the same event from different platforms and displays cross-platform coverage and aggregated heat, which clearly distinguishes it from sibling tools like get_latest_news, search_news, and get_news_by_date.

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 clearly implies the tool is for when you need deduplicated, cross-platform aggregation of the same event, and provides parameter defaults like 'not specified uses all platforms' and 'similarity threshold controls strictness.' However, it does not explicitly name alternative tools or state when not to use it, so it lacks explicit exclusions or alternative suggestions.

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