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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false. The description adds the useful 'A constant' trait, indicating it will not change, and positions it as a foundational reference. This is beyond what annotations provide, though it does not describe return format or any potential limitations.

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 sentences, front-loaded with the core purpose, then a concise explanation of its significance. Every word earns its place; no filler or repetition.

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 tool with no output schema, the description provides the essential context: what the sources are, that it includes AI crawlers, and its role in interpreting lookup_domain. It is complete enough for a model to decide when to call it, though it doesn't detail the output structure beyond saying 'what each source means.'

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 per the rubric. The description does not need to explain parameters, and the input schema is empty, so no additional semantics are required.

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 exactly what the tool returns: 'What Domduck reads and what each source means.' It is specific about the content (including the eight AI crawlers whose robots.txt policy is tracked) and clearly distinguishes it from siblings by referencing lookup_domain and the need to read this first.

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?

Explicitly says when to use it: 'Read it first' and why: 'it is what lets a model interpret lookup_domain without guessing.' This provides clear context and an alternative (lookup_domain) to not need description guessing.

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