like_post

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

No annotations are provided, so the description carries the full burden. It discloses that the action is performed as the authenticated user, but does not describe side effects, idempotency, errors, or whether liking an already-liked post is treated as a no-op.

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 a single, front-loaded sentence that states the action and then clarifies the parameter. Every word earns its place, with no redundant or vague filler.

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?

This is a simple one-parameter tool, and the description covers the core action, the authentication context, and the accepted input format. An output schema exists, so return values do not need explanation. Minor gaps around behavioral edge cases keep it from a 5.

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?

The input schema only defines `post` as a string with no description, giving 0% schema coverage. The description fully compensates by explaining that `post` is either a post URN or a linkedin.com post URL, adding essential meaning beyond the schema.

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 uses a specific verb and resource: 'Like a LinkedIn post' with the context 'as the authenticated user.' This clearly distinguishes it from sibling tools like comment_on_post or create_post.

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 implies usage when the agent needs to like a post and notes that it operates as the authenticated user. However, it does not explicitly state when to avoid using it or mention alternatives like comment_on_post versus like_post.

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