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

Annotations already declare readOnlyHint/destructiveHint=false, so the safety profile is covered; the description goes further by disclosing the metric (median engagement rate per post), the accompanying sample size, the reason for it (small-n formats should be read with care), the insufficient-data response ('there isn't enough data yet'), and platform-specific coverage limits.

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 user-question framings, then methodology, then sibling routing — a sensible order with no wasted clauses. The three near-synonymous example questions are slightly redundant but serve query-matching discoverability, so the minor verbosity is defensible.

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?

With no output schema, the description carries the return-value burden and does: it explains the ranking, the metric, the per-format post counts, and the empty/insufficient-data case. Combined with the read-only annotations and fully documented parameters, an agent has everything needed to call and interpret it.

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 100%, so all four parameters — including the period/start_date/end_date interplay and enum values — are already fully documented in the schema. The description only implies that a platform must be supplied and adds no format, default, or override semantics beyond it, so the 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?

States a specific verb and resource — 'Ranks post formats (text, image, video, etc.) by engagement' for a given platform — and frames it around the exact user questions it answers. It is clearly distinguishable from get_analytics_summary (overall numbers) and list_top_posts (single posts), both of which it names.

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 explicit triggering questions ('what type of post works best?', 'do images or videos perform better?') plus explicit routing to alternatives for adjacent needs. The LinkedIn scope caveat ('member profiles' posts only, not company pages') further constrains when results are valid.

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