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

Annotations declare destructiveHint=true and idempotentHint=false, but the description adds genuinely new behavioral context: the conversion rule survives detachment (only the campaign stops reporting/optimizing), automated bidding can halt delivery, and confirm=false yields a preview. That is exactly the kind of consequence detail annotations cannot convey.

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 the core action, then scoping clarification, then a clearly marked WARNING block, then per-parameter notes. Dense and every element is useful, though the '(ad set)' parenthetical and the illustrative example add a little length that could be trimmed.

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?

An output schema exists so return values need no explanation, and the description covers prerequisites, safety consequences, preview mode and the inverse relationship to a sibling. Nothing an agent needs to invoke this mutation safely is missing.

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 the load; it explains conversion_id ('the numeric id from list_linkedin_conversions') and confirm ('false = preview'), which are the two non-obvious parameters. account_id and campaign_id are left implicit, but they are self-evident within LinkedIn tooling, so the compensation is substantial though not total.

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 ('Removes a conversion tracking rule from a LinkedIn campaign (ad set)') and explicitly positions itself as the inverse of attach_linkedin_conversion. The 'rule itself is NOT deleted' clause crisply distinguishes it from a deletion tool such as delete_linkedin_campaign.

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?

Gives an explicit use case ('clean up a badly scoped rule, e.g. a site-wide All Pages conversion that inflates counts'), a prerequisite check (list_linkedin_conversions -> associated_campaigns first) and a warning about the MAX_CONVERSION case where detaching the last conversion stops delivery. When-to-use, when-not-to-use and the alternative are all present.

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