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

Annotations cover the safety profile (readOnlyHint, openWorldHint), and since there is no output schema, the description usefully discloses the return type: a probability that the answer is yes. It does not mention limits like the 60000-char text cap, but that is documented in the schema.

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 tight sentences: capability and return type front-loaded, followed by a concrete usage example. No filler.

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?

No output schema, so the description's disclosure of the probability return is valuable, but with 4 parameters at 50% schema coverage the roles of no_means/yes_means go unexplained. Adequate but with a clear gap for the optional steering parameters.

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 description coverage is only 50% (question and no_means have no schema descriptions). The description adds no meaning for those parameters and only implicitly signals that text+question are the inputs via its example. It does not explain how no_means/yes_means steer the judgment.

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?

States a specific verb+resource (ask a yes/no question about text) and the return value (probability of yes). The 'yes/no' framing implicitly distinguishes it from siblings like judge_choice and judge_score, though no sibling is named explicitly.

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?

Gives clear usage context ('checks and gates') plus a concrete example question, which tells an agent when this tool fits. It lacks explicit exclusions or named alternatives (e.g. when to prefer judge_choice over judge_yes_no).

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