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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, covering the safety profile. The description adds behavioral detail beyond annotations: it explains entity resolution (tickers, companies, topics), the fallback to freshest stories when nothing is recognized, and that each result carries an id for the fetch tool. This provides useful context without contradicting the annotations.

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 three sentences, front-loaded with the core purpose, and includes necessary usage guidance, behavioral notes, and a pointer to an alternative. No fluff or redundancy. It is concise while covering essential information, though it could arguably be slightly tightened without losing meaning.

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 simplicity (one parameter), existing output schema, and annotations that cover safety, the description is nearly complete. It covers query interpretation, fallback behavior, and the link to the fetch tool, and it names an alternative. The only minor gap is that it doesn't explicitly describe the output format, but the output schema handles that, so the description is sufficient.

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 description coverage is 100% (the 'query' parameter has a basic description). The tool description adds semantic meaning by explaining that the query is natural-language and how it is interpreted (resolved to structured filters, with a fallback). This goes beyond the schema's simple 'Natural-language search query' description, giving the agent a richer understanding of the parameter's role.

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 a specific verb and resource: 'search AlphAI's AI-enriched financial news' with a natural-language query. It explicitly distinguishes itself from the sibling alphai_news_search by saying 'For precise filtered queries prefer alphai_news_search.' This makes the tool's scope and differentiation clear.

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 explicit usage guidance: it explains that the query is natural-language and that known entities are resolved to filters, while a query with no known names returns freshest high-relevance stories. It also explicitly names the alternative (alphai_news_search) and the condition for preferring it ('precise filtered queries'). This covers when and when not to use the tool.

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