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

With no annotations, the description carries the full burden and does well: it discloses the 3-draft cap, that search spans all of LinkedIn, that it draws on saved comment_topics, that the caller must display every draft in full, and that nothing is approved or scheduled automatically. It stops short of saying whether the drafts persist and become visible to list_comment_drafts, or what happens when no relevant posts are found.

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?

Front-loaded with purpose, then a labeled use/do-not-use block, then post-call output instructions — a clean hierarchy. The mandated display template is somewhat verbose and the appended HTTP response boilerplate adds little, but nearly every line changes agent behavior.

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?

There is no output schema, and the description compensates by naming the returned fields (author_name, post_text, comment_text) and prescribing exactly how to render them. For a zero-parameter, no-annotation tool, an agent has everything it needs to call it and act on the result.

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 tool takes zero parameters, which is the baseline 4 case; there is nothing for the description to disambiguate. Its implicit note that topics come from the user's saved comment_topics usefully explains where the input actually comes from, given the empty 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?

States a specific verb and resource ('Generate up to 3 LinkedIn comment drafts on relevant posts') plus the mechanism ('searching all of LinkedIn for the user's saved comment_topics'). It explicitly distinguishes itself from run_company_commenting_pipeline and, by scope, from search_posts_by_topic.

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?

Provides an explicit USE THIS block with three realistic utterances, plus a DO NOT USE rule naming the situation (user names specific companies) and the correct alternative tool. Nothing about tool selection is left to inference.

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