search_news

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

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It explains the return format, data display strategy, and the effect of threshold on filtering. It also notes that include_url defaults to False to save tokens. However, it does not discuss rate limits, authentication requirements, or whether the tool has any side effects.

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 long but well-structured with clear sections (Args, Returns, important notes, AI instructions, calculation rules, examples). It is front-loaded with the core purpose. While some details could be condensed, the structure aids readability. The inclusion of examples and rules adds value without being overly verbose.

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?

Given the tool's complexity (8 parameters, multiple modes, date handling) and the presence of an output schema (not shown), the description is highly complete. It covers all parameter semantics, usage guidelines, edge cases (e.g., single-day queries), and even provides explicit examples. The description leaves no obvious gaps for an AI agent to use the tool 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?

Schema description coverage is 0%, meaning the schema provides no parameter explanations. The description compensates fully by detailing each parameter, including search_mode values, date_range format and examples, platform usage, limit, sort_by, threshold, and include_url. This provides rich semantic context beyond the raw 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 clearly states it is a unified search interface supporting multiple search modes. It defines the tool's purpose as searching news with various modes like keyword, fuzzy, and entity, making it distinct from sibling tools such as get_latest_news or 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 provides detailed guidance on when to use each search mode and how to handle relative date expressions. It includes examples of mapping user phrases to date ranges. However, it does not explicitly compare against sibling tools, so an AI agent might be unclear when to choose search_news over alternatives like get_news_by_date.

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