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

With no annotations, the description must carry behavioral context; it helpfully discloses that counts are real and include zeros, and that AI visits are scoped to the last 30 days by client. However, it does not state read-only behavior, whether results are cached or sampled, or what the response structure looks like.

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 definition is a single front-loaded sentence with no fluff, and the metric list directly supports selecting and interpreting the tool. The dense enumeration is acceptable because each item specifies a returned count.

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 tool, the description lists the important returned metrics and time scope, which is enough to call it correctly. Because there is no output schema, it could do slightly more to describe the result shape, so it is not fully 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, so there are no parameter semantics to document; per the rubric this sets the baseline at 4. The description does not need to explain any inputs because none exist.

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 explicitly identifies the resource as an aggregate of platform counts and enumerates the metrics such as registered AIs, verified businesses, signed statements, and open jobs. It is clear this is a stats snapshot, though it does not explicitly contrast with siblings like get_platform_info or the list_* 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?

It gives no explicit when-to-use guidance or exclusions relative to siblings such as get_platform_info, get_log_state, or list_agents. The phrase 'Real counts, zeros included' implies aggregate reporting, but the agent must infer that this is the tool for summary counts rather than detailed listings.

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