linkedin_unfollow_company

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

With no annotations provided, the description carries the full burden and excels at it. It discloses the two-step confirmation flow, the lack of pagination in the followed-companies list and its consequences, the confirmation method (reloading the list and checking the total count drop), and the 'unknown' vs 'not followed' distinction. It even explains why a partial list makes absent rows unreliable evidence. This is exemplary behavioral disclosure.

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 long, the description is tightly structured and every sentence earns its place. The key warnings are front-loaded and emphasized with caps, and the paragraphs each address a distinct behavioral aspect. It is verbose only where necessary; the complexity of the tool justifies the length, and the structure makes it scannable.

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 an output schema and non-trivial behavioral caveats, this description is complete. It covers prerequisites (get the ID), limitations (list is partial), verification (count drop), and side-effect asymmetry. Nothing an agent needs to call it correctly or interpret results 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 parameters are already documented. The description adds contextual meaning: company_id is clarified as the numeric id printed by the followed list (not the name), and confirm_token is explained as the preview vs final confirmation flow, with an empty value meaning preview. This goes beyond the schema's basic 'leave empty to preview' by tying it into the overall two-step process, justifying a 4 over the baseline 3.

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 opens with a precise verb+resource ('Stop following one company Page') and immediately differentiates from siblings by referencing the shared two-step shape and five gates, and by naming the asymmetry with linkedin_follow_company. It leaves no ambiguity about what the tool does and how it differs from related tools.

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 explicit when-to-use guidance: call linkedin_followed_companies first to obtain the numeric ID, explains that names are refused, and details the list incompleteness that makes a 'unknown' verdict possible. It also states the asymmetry (can unfollow but not follow) and directs to the unimplemented follow tool for the inverse, leaving no room for misuse.

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