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

Beyond the annotations, the description discloses the important behavior: the work runs once in the background, has persistent side effects (writes to Puley history and sends an email), and is polled by repeating the identical request. That is meaningful disclosure for an async job. Gaps remain around expected duration, poll cadence, failure modes, and there is a mild tension with idempotentHint=false, since repeated identical calls are described as returning existing progress rather than launching new work.

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 pipeline sentence is long but earns most of its length, and the polling note at the end is high value. However, the parameter glosses are jammed into the prose mid-paragraph as sentence fragments ('what: what is sold, and where. business: its name or website.'), which reads as an afterthought and hurts scannability.

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?

For an async tool with no output schema, the description does the necessary work: it explains that the result is a plan saved to history and emailed, and that intermediate calls return progress. The remaining shortfall is the undocumented 'country' input and the absence of any sense of how long the background run takes.

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%, so the description must carry parameter meaning, and it only partly does. 'what: what is sold, and where' is genuinely ambiguous about whether location belongs in 'what' or in the separate 'country' parameter, and 'country' (default 'US') is never mentioned at all despite materially changing which sites get quoted.

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 names a concrete outcome and the mechanism behind it: query ChatGPT and Gemini for recommendations, find the sites the answers quote, read the user's site, and write a plan to get named. That is far more specific than the passive name 'get_recommended_by_ai' suggests. It never distinguishes itself from the confusingly similar sibling 'ai_recommends', which an agent would plausibly confuse with this tool.

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 stated: the tool is for someone who sells something and wants to be named in AI answers, and the final sentence tells the agent how to poll ('The same request again returns its progress or the finished plan'). There are no explicit when-not conditions and no routing away from siblings such as 'ai_recommends' or 'company_brief'.

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