linkedin_mark_notifications_read

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

Goes beyond annotations by describing the internal mechanism ('server loads and scrolls the notifications page'), which implies non-trivial behavior such as potential delays or side effects. Annotations only state it is not read-only and not destructive, so this added context is valuable.

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?

Two concise sentences. The first front-loads the action, the second provides the necessary mechanism context. No wasted words; every clause contributes meaning.

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 simple mutation tool with no parameters and no output schema, the description covers the action, the reason for the non-standard approach, and the method. It could have mentioned potential side effects (e.g., marking all notifications read) but the mechanism implies this, so it remains fairly complete.

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, and the schema coverage is 100% (trivially). The description doesn't need to add parameter details, and the baseline for zero-parameter tools is 4. It adds no extra parameter information, which is appropriate.

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?

Begins with the clear verb+resource pair 'Mark notifications as read', which directly states the tool's function. It also distinguishes itself from siblings like 'get_notifications' and 'delete_notification' by explicitly targeting the unread badge mechanism.

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?

Provides clear context by explaining that LinkedIn lacks a bulk API and this tool simulates how LinkedIn itself clears the unread badge. This implies the tool should be used when the goal is to clear unread notifications, though it does not explicitly name alternatives or exclusions.

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