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

Annotations indicate the tool is not read-only and not destructive, and the description matches by explaining it creates a design and then a scheduled post. It goes beyond annotations by disclosing the 15–60 second wait, the failure behavior and designId reuse path, the paid-plan requirement for AI images, quota consumption, added latency, and the best-effort nature of image generation. No behavioral contradictions with the annotations exist.

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 front-loaded with the core purpose and then provides necessary orchestration details, failure handling, and per-postType guidance. It is long but every section serves a real instructional need; minor redundancy (e.g., repeating the scheduling-fields instruction) keeps it from being perfectly concise.

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 the tool's complexity and the existence of an output schema, the description covers the essential context: the import-then-schedule workflow, how to avoid re-importing on failure, which sibling tools to consult first, defaults for optional parameters, video settings fallback behavior, and AI image generation caveats. No critical usage context appears 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?

The schema already covers 100% of parameter descriptions, including the slide vs. slides distinction, videoSettings vs. postSettings, and responseType. The tool description mostly restates these relationships rather than adding new parameter-level meaning. It adds some high-level orchestration context, but per the rubric baseline for full schema coverage, this is adequate but not exceptional.

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 this is a convenience tool that imports content, waits for it to finish, then creates a scheduled post attaching the resulting design. It distinguishes itself from sibling import and scheduling tools by naming the mirrored tools (postnitro_import_carousel / postnitro_import_video / postnitro_import_image) and postnitro_create_scheduled_post, so an agent knows exactly what this composite operation does.

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 gives explicit, actionable guidance: which postType maps to which slide input, that postnitro_get_import_template should be called first, that optional parameters can come from defaults, and that if scheduling fails after import the returned designId can be reused with postnitro_create_scheduled_post without re-importing. It also tells the agent to provide scheduling fields exactly as in postnitro_create_scheduled_post, leaving no ambiguity about when to use this tool.

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