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

With no annotations, the description carries the full behavioral burden. It discloses the nature of the output (a reference bundle of models, edge cases, laws, anti-patterns) and implies a read-only lookup, but says nothing about freshness, auth needs, rate limits, or whether unknown domains error or fall back.

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 purpose clause is nicely front-loaded in one sentence, but the entire second sentence duplicates the domain enumeration already present in the input schema, adding bulk without new information.

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?

With no output schema, the description must stand in for return semantics; it names the four content categories it returns, which is enough to set expectations, but gives no indication of the response shape or size for the 36 supported domains.

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 100% and the single required parameter is already documented in the schema, so the baseline is 3. The description merely restates the same domain list and adds no format, casing, or defaulting detail beyond what the schema provides.

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 gives a specific verb (返す / returns) plus a concrete inventory of what is returned (must-have models, edge cases, required laws/regulations, anti-patterns) scoped to a domain. It is clear what the tool produces, but it never distinguishes itself from close siblings such as b612_patterns or b612_principles, which plausibly return overlapping knowledge content.

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?

There is no when-to-use statement, no prerequisite, and no routing guidance relative to the seven sibling tools. The domain list is a parameter enumeration, not usage guidance, so the agent must infer the trigger condition on its own.

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