linkedin_auth_status

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

With no annotations provided, the description carries the full burden and does so excellently. It discloses the internal behavior: makes up to two requests, uses the identity endpoint, treats cookies as unreliable, and explicitly defines the null case as 'unknown' rather than 'signed out'. This level of detail is transparent and goes well beyond basic expectations.

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 structured with a clear opening statement, followed by detail and bullet-point outcomes. Every sentence adds value: it explains the measurement method, the meaning of each outcome, and the cost. It is thorough without being verbose, and the front-loaded core statement makes it easy to grasp quickly.

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?

Given that the tool has no parameters, an output schema exists, and the description thoroughly explains the semantics of each outcome, nothing is missing. It also covers edge cases (e.g., cookies from signed-out users) and costs, making it complete for an agent to use 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?

The tool has zero parameters, so the description correctly does not discuss any. Per the calibration rules, a baseline of 4 is appropriate since there are no parameters to explain and the schema coverage is 100% (empty schema). The description doesn't need to add anything beyond that.

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 clearly states the tool's purpose: to report whether there is a live LinkedIn session, and it specifies the exact method (authenticated GET /voyager/api/me). It distinguishes itself from siblings like linkedin_session_info by emphasizing it measures actual authentication status, not guessed from cookies.

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 effectively communicates when to use this tool—whenever an agent needs to determine authentication status. It explains the methodology and the three possible outcomes, making it obvious how to interpret results. However, it does not explicitly name alternative tools or state when not to use it, so it misses the highest bar for usage guidance.

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