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

With no annotations, the description carries the full burden of behavioral disclosure. It indicates the tool is passive (no side effects) and describes the output contents. It lacks explicit mention of authentication requirements or edge cases, but for a zero-parameter read-only tool, this level of disclosure is adequate.

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 two sentences long, with the action and result stated immediately in the first sentence. No wasted words, and the secondary sentence adds useful context without redundancy.

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 the tool's low complexity (no parameters, no output schema), the description covers the essential information: what it does, what it returns, and a typical use case. It could be slightly more explicit about the return format, but for a simple status-like tool, it is reasonably 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 the baseline is 4. The description adds context about what the output represents, which is sufficient since there are no parameters to document.

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 uses a clear verb ('Returns') and specifies the resource ('the Observatory's passive classification of the current request') and the output ('agent-likelihood score and the header signals behind it'). This clearly distinguishes it from siblings like check_in or get_census by focusing on passive classification rather than active checks.

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 provides a clear use case ('Useful for testing your own stack'), giving context for when to use the tool. However, it does not explicitly mention exclusions or alternatives, so it does not fully meet the 'when/when-not' standard.

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