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

With no annotations, the description carries the full burden, and it does substantial work: it discloses the returned payload's shape (post, author, counts, timestamp, `comment_url`), ordering and page size ('newest first, up to 100 a page, with `next_cursor`'), caching behavior, and cost/refund policy ('7 credits, cached or not; a failed call is refunded'). It still omits auth requirements, rate limits, and behavior for nonexistent or private profiles.

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?

One dense but well-ordered paragraph: scope first, then per-type behavior, then payload, then pagination, then purpose, then input rule, then cost. Every sentence earns its place, though the credit/caching information is partially duplicated by the `fresh` parameter description.

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 6-parameter, unannotated tool with no output schema, the description compensates well by describing the return shape, pagination, and billing. The remaining gap is operational context an agent may need before calling: authentication requirements and failure modes other than credit refund.

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%, so the baseline is 3, but the description adds constraints the schema cannot express: the mutual exclusivity of `url` and `vanity_name` (required is empty, so the schema itself permits both or neither), and that `posted_within` only applies with `type=comments`. These are real, decision-relevant additions.

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+resource with an explicit scope: 'what one person did recently on other people's posts', split by `type=comments` vs `type=reactions`. This implicitly separates it from person-agnostic siblings like get_linkedin_post_comments and get_linkedin_post_reactions, which operate on a single post rather than a single person.

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 a concrete use case ('sales signals: who a prospect engages with, and on what') and a hard input rule ('give exactly one of `url` or `vanity_name`'), which orients the agent well. It does not, however, name an alternative sibling or state when not to use it (e.g. use get_linkedin_post_comments when you already have the post).

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