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

Beyond annotations, the description discloses significant behavioral details: the mapping is written to the integration and rebuilt immediately, but a separate re-export is still required; rule_id and platform_code/constant are mutually exclusive; setting a source detaches any existing rule; rule_enabled semantics are fully explained; and a dangerous empty-integration outcome is warned about. This exceeds what annotations alone could 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?

The description is lengthy but every sentence earns its place: each block covers a distinct concern—targeting, source mapping, rule behavior, save-and-rebuild workflow, prerequisites, and return values. It is dense, well-organized, and free of filler.

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 a tool with 9 parameters, cross-tool dependencies, post-condition side effects, and edge cases, the description is remarkably complete. It covers prerequisites, ambiguous cases like rule_enabled without a rule, mutual exclusivity, rejection reasons, and required follow-up actions, leaving little unresolved for correct invocation.

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 coverage is 0%, so the description carries full responsibility for parameter meaning. It explains all nine parameters: code's origin, platform_code/constant sources, default_platform_code/default_constant fallback behavior, rule_id attach/detach semantics, rule_enabled edge cases, project_id optionality, and marketplace_id provenance. It also adds the empty-string clearing convention.

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 action on a specific resource: 'Set where ONE of a marketplace's output attributes gets its value' and brands it as 'the write half of solving the attribute form.' This clearly distinguishes it from read-side tools like get_marketplace_attributes and from ad/feed mapping siblings.

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 when-to-use and when-not-to-use guidance: map only attributes returned by get_marketplace_attributes, run refresh_marketplace_category_attributes first for category-specific attributes, and confirm identifier source population via get_project_profile/preview_products. It also references the follow-up repair_marketplace or run_marketplace_operation for re-export, giving clear context versus alternatives.

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