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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, and the description reinforces 'Read-only.' The added value is real: it discloses the cost model ('One LinkedIn call per page of ~10 posts; keep to a few pages') and the return shape (text, engagement, author with headline, member id, relationship degree). It stops short of 5 only because pagination/cost behavior is described lightly.

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 and returns, then filters, then follow-up tools. Dense but nearly every clause carries information; the follow-up tool routing is arguably a long tail but earns its place by guiding a multi-step workflow.

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 9-parameter, 2-required read tool with an output schema and full annotation coverage, the description supplies everything an agent needs: when to use it, keyword syntax, filter dimensions, pagination cost, and the downstream tools to chain. Output schema handles return values, so no gap remains.

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 baseline is 3. The description goes beyond the schema by explaining LinkedIn keyword operator syntax ('exact phrase', OR, AND, NOT) and by summarizing the filter axes (recency, author job title, company, industry, content type) plus the start offset semantics.

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 (search LinkedIn posts) and scopes it clearly against siblings by likening it to the LinkedIn Posts tab ('who is talking about a topic right now'). An agent can distinguish it from scrape_search, list_user_posts, and scrape_my_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?

Explicitly names when to use it (buying signals, hiring, pain points, competitors) and routes the agent to concrete follow-ups: scrape_post for engagers, scrape_profile to qualify an author, send_invitation with author_linkedin_id. It even prescribes a limit ('keep to a few pages'), which is actionable usage guidance.

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