Scrape the LinkedIn feed

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

The description substantially exceeds the annotations' coverage. It discloses the read-only nature (never likes/comments/reposts, no confirm, no quota), explains the lazy-loading with human-like pauses and the time implication for large counts, de-duplication by permalink, skipping of sponsored cards, and that fewer-than-requested results is normal. This rich behavioral detail helps the agent anticipate side effects and performance without contradicting the annotations.

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?

Though longer than average, every sentence earns its place. The opening sentence states the core purpose, then parameter semantics, behavioral safety, de-dup/skipping logic, and the normal-result caveat are each addressed succinctly. The structure is front-loaded and contains no filler or repetition.

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 tool with one parameter and no output schema, the description covers everything an agent needs: what it returns, how it behaves, potential pitfalls (time, fewer results), and safety guarantees. There are no meaningful gaps that would prevent correct invocation.

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 0%, so the description must fully compensate. It does: it explains that count is how many posts to return, defaults to 10, has a max of 50, and that a larger count involves scrolling and can take a minute or more. This goes far beyond the schema's bare integer min/max, giving the agent the context needed to choose an appropriate value.

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?

The description states a specific verb ('reads') and resource ('signed-in member's home feed'), and enumerates the exact output fields (author name/headline, post text, permalink, reaction/comment counts, timestamp). This clearly distinguishes it from sibling tools like linkedin_scrape_profile, which targets profiles, and linkedin_create_post, which writes.

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 unambiguously communicates that this tool is for reading the home feed, which sets it apart from siblings (profiles, jobs, messaging). However, it does not explicitly name alternative tools or state conditions when this tool should be avoided (e.g., 'if you need profile data, use linkedin_scrape_profile'), leaving that inference to the agent.

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