current_visitors

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

With no annotations, the description carries the burden and does a good job: it defines the rolling window, uniqueness semantics, and exact return payload. It does not explicitly mention read-only status, auth needs, or failure modes, but those gaps are modest for such a simple metric tool.

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?

One tight sentence with an embedded JSON example. The key behavioral facts are front-loaded, and every word earns its place.

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 one-optional-parameter read tool with no output schema, the description plus the detailed schema are nearly sufficient. The inline JSON example compensates for the missing output schema, though the description itself leaves multi-site key handling to the schema.

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 schema already fully documents the optional site parameter. The description adds no extra parameter-level meaning, making the baseline 3 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?

The description states a specific verb ('count'), a precise resource ('unique visitors active in the last 5 minutes for a site'), and provides a concrete return example. This clearly distinguishes it from time-series or top-N sibling 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 implies the use case—current/live activity versus historical trends—through 'realtime' and the 5-minute window. However, it never explicitly says when to prefer this tool over siblings like visitors_over_time, nor gives exclusions or alternatives.

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