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

Annotations already cover the safety profile, but the description adds real behavior beyond them: conjunctive matching over every word, a five-word ceiling, newest-first ordering, and exactly what each result carries (post_id, date, snippet, headline numbers, link). It doesn't say what happens when a query exceeds five words or how the 25-result cap interacts with result paging.

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 purpose, then search semantics, then return shape, with no filler sentences. The three quoted example queries and the LinkedIn/off-platform scope clause make it longer than strictly necessary, though each still contributes.

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?

Six parameters with one required, no output schema, so the description must carry return information, and it does: result ordering, per-match fields, and how post_id feeds downstream tools. Search scope, defaults, and matching rules are all present, leaving nothing an agent needs in order to call this correctly.

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 the per-parameter semantics (author, platform enum, date bounds, limit) are already fully documented. The description adds only a little on top, namely case-insensitivity and the five-word cap on `query` and the "no dates means all time" default. Baseline 3 is appropriate when the schema carries this much weight.

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 ("Find published posts by what they say") and immediately bounds the corpus: every post on the workspace's channels, including LinkedIn imports and posts made outside DemandBird. The example phrasings and the follow-up pointer to get_post_analytics/tag_published_post make it distinguishable from list_posts, list_recent_posts, and get_post 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?

Gives concrete when-to-use triggers via three natural-language examples and a clear usage rule ("use a few distinctive words, not a sentence"), plus the default that omitting dates searches all time. It does not explicitly exclude the sibling listing tools (list_posts, list_recent_posts) for cases like browsing without a keyword, so the agent must infer that boundary.

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