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

The annotations already mark this as a read-only, idempotent, open-world operation, so the description's job is to add behavior on top. It does so: a 10-post per-call cap, the exact returned fields, the caveat that LinkedIn returns loosely related posts, and how keywordsInText is computed. That is material behavior an agent could not infer from the structured fields.

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 purpose, then filters, then return shape, then follow-up routing. Nearly every sentence earns its place, though the return-value enumeration is dense enough that an agent must read to the end to find the useful pagination note.

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, so the description must describe results, and it does: per-post fields, the 10-post limit, and the caveat about loosely related matches. Pagination, filtering, and downstream tool routing are all covered, so nothing needed to call it correctly is 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?

Schema coverage is 100%, so the schema already documents all four parameters. The description still adds matching semantics, namely that keywordsInText is true only when the post text contains every keyword (or one of several joined with OR) and how exact phrases work, which meaningfully shapes how the agent writes queries.

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?

A specific verb plus resource ('Search LinkedIn posts by keywords') is followed by three concrete use cases (finding people asking for a service, hiring announcements, topical posts). That is enough to separate it from siblings like linkedin_search_people, linkedin_search_jobs, and linkedin_get_company_posts without opening any schema.

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 clear usage context, routes the agent to linkedin_get_post_reactions and linkedin_get_post_comments for follow-up detail, and explains when to pass nextCursor. It stops short of an explicit 'do not use this for X' exclusion, such as distinguishing it from search_people or company posts.

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