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

Goes well past the readOnly/openWorld annotations: discloses the comparison-period semantics, that LinkedIn totals include posts not made through PostPen, that figures are observations not causes, and that with LinkedIn disconnected only the networks block returns. Those are exactly the non-obvious behaviors an agent needs before trusting the output.

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 what is shown, then interpretation, then caveats, then the networks fallback — a sensible order. It is dense and long for a one-parameter read tool, but each clause (vs_usual ratio meaning, non-PostPen inclusion, connected-account coverage) carries real information.

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, so the description must convey the return shape — and it does, naming recent_posts fields (engagement_rate, comment_share, attributes, vs_usual with a 1.3 = 30% above usual example) and the networks payload including the error/status case. An agent can parse and interpret the response without guessing.

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 100% and the single parameter already documents the 7–90 range and the equal-length prior comparison window; the description merely restates 'last N days' and the equally long preceding period. No extra syntax or default guidance beyond the schema, so baseline 3 applies.

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?

Opens with a concrete verb+resource ('Shows whether each LinkedIn account is growing') and enumerates the metrics returned, which separates it from get_post_stats and list_posts. It never names a sibling to contrast with, so differentiation is inferable rather than explicit.

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?

Explicitly says 'Use it to plan content: compare the change in results with the change in how much was posted, then look at which recent posts beat their account’s usual' — a clear intended workflow. It gives no when-not-to-use or alternative tool for simple per-post numbers, leaving that routing to the agent.

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