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

Annotations already declare readOnlyHint=true, openWorldHint=false, and destructiveHint=false. The description adds useful behavioral context: it reads by rules, does not guess when a question cannot be parsed, and returns a 'could not read' message with example phrasings. It also discloses that the response includes evidence tools/arguments and sources.

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 compact: two sentences with no filler. The examples are dense but earn their place by clarifying scope, and the core behavior is front-loaded before the failure-mode sentence.

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 an aggregator tool with one natural-language parameter, no output schema, and safety annotations, the description is largely complete: it covers inputs, outputs, evidence returned, and failure behavior. It leaves a minor gap by not explicitly routing the agent between this tool and the individual sibling tools.

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 coverage is 100%, so the single question parameter is already documented. The description adds meaningful scope beyond the schema by giving four concrete example question types, showing that the parameter accepts natural-language queries about dates, rokuyo, solar terms, and business-day offsets.

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 a specific action (returns in one go) for a specific resource (one Japanese calendar question) and names the return components: answer, evidence tools/arguments, and sources. The examples clarify the calendar domain and breadth. It does not explicitly name sibling tools, so it falls short of full sibling differentiation.

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 examples imply when the tool is useful for natural-language calendar questions, and the failure behavior describes one boundary condition. However, it does not say when to use this aggregate tool versus specific siblings like add_days, find_dates, or get_rokuyo_hours, nor does it state exclusions beyond unreadable questions.

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