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

Annotations already declare readOnly, idempotent, non-destructive, and closed-world, so safety is covered. The description adds that the tool reports concrete issues to fix rather than editing or publishing, which is useful confirmation that nothing is mutated. It stops short of describing issue severity or ordering.

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?

A single dense sentence front-loads the use case, then the checklist, then the return value. No filler and no repetition of the title or annotations.

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?

No output schema exists, and the description compensates by stating the tool returns concrete issues to fix, which is enough for an agent to know it is diagnostic rather than transformative. The shape of the returned issues (list, score fields, severity) remains unspecified.

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?

There is one required parameter and schema coverage is 100%, with the schema stating "The full post text." The description implies the post text is what gets measured against the 3000-character limit, but adds no format guidance beyond the schema. Baseline 3 applies when the schema does the documenting.

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?

Specific verb (check) plus resource (a LinkedIn post) plus an enumerated list of what is inspected: character count, see-more cutoff, hook score, unrendered Markdown, placeholders, AI-sounding phrases. The pre-publish framing cleanly separates it from siblings that generate or format content.

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?

"Check a LinkedIn post before publishing" gives a clear triggering moment in the workflow. It does not name alternatives such as preview_linkedin_post, so the agent must infer the boundary between validating and previewing rather than being told.

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