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

Beyond the readOnlyHint annotation, the description discloses that in-context figures go stale after queueing or approving sends, making a re-call necessary. It also explains the return value's configurable_limits semantics (default, ceiling, custom cap, and that None means using the default), adding meaningful behavioral context.

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 front-loaded with the core purpose, followed by concrete when-to-use guidance and a structured returns section. Each sentence earns its place; there is no fluff or repetition of schema/annotation data.

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 zero-parameter, read-only tool with no output schema, the description is complete: it states what the tool does, when to call it, when to call it again, what the return contains, and which sibling covers per-contact queue details. Nothing essential 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?

The tool has zero parameters and the schema description coverage is 100%, so there are no parameter semantics to explain. The baseline for zero-parameter tools is 4; the description adds no unnecessary parameter information.

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 specific verb and resource: 'Check LinkedIn usage limits and invite queue counts.' It clearly distinguishes itself from the sibling check_email_daily_usage (email usage) and explicitly scopes out manage_linkedin_invite_queue's per-contact queue status, so an agent can tell which tool is which.

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?

Usage guidance is explicit: call before sending multiple LinkedIn messages/InMails/connection requests to get a fresh read, and re-call after queueing or approving sends because prior figures become out of date. It also names the exact alternative (manage_linkedin_invite_queue with action='status') for per-contact queue details.

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