trends_research_generate_trends_action

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

No annotations are provided, so the description must fully disclose behavioral traits. It mentions using Perplexity Sonar Pro and GPT-5 Mini, and lists the six sections. However, it does not disclose potential side effects (e.g., API rate limits, cost implications) or behavior on invalid inputs (e.g., missing required fields). The transparency is adequate but not comprehensive.

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 sentence that front-loads the main action and lists all sections. It is reasonably concise, though slightly long due to enumerating all six sections. Every piece of information is useful, but the sentence could be broken for better readability.

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 7 parameters, no output schema, and no annotations, the description explains the digest structure (6 sections) and mentions the tools used. However, it does not specify the output format (e.g., JSON, text), typical response time, or any error handling, leaving some gaps for complex research 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?

Schema description coverage is 86%, so baseline is 3. The description adds value by explaining that optional parameters (seller_product, seller_icp, seller_website, seller_competitors) enable personalized sections (e.g., 'What This Means for You'), which goes beyond the schema's field descriptions.

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 the tool generates a structured 6-section trends research digest for any industry, specifying the verb ('Generates') and resource ('trends research digest'). It lists all six sections (Top Trends, Industry Signals, etc.), which distinguishes it from sibling tools like industry_research_generate_research_action that may produce different outputs.

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 implies usage for generating industry trend research but does not explicitly state when to use this tool versus alternatives like aeo_seo_research_generate_research_action or industry_research_generate_research_action. No exclusions or when-not-to-use guidance is provided.

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