japan-seasons-mcp

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

Annotations indicate readOnlyHint=true and idempotentHint=true, covering safety and idempotency. The description adds valuable context beyond this: it specifies the tool's behavior ('based on forecast peak dates'), output type ('cities'), and constraints ('overlap the trip'). However, it doesn't mention potential limitations like rate limits or data freshness, which could be useful for a forecasting tool.

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 front-loaded with the core purpose in the first sentence, followed by usage guidelines and exclusions. Every sentence earns its place by clarifying when to use the tool, what it returns, and alternatives, with zero wasted words. The structure is logical and efficient.

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 moderate complexity (forecast-based city matching), annotations cover safety aspects, but there's no output schema. The description adequately explains the return value ('cities') and context, though it could benefit from mentioning output format details (e.g., list structure or ranking). It's mostly complete but has a minor gap in output specification.

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%, with both parameters (start_date and end_date) well-documented in the schema. The description adds minimal parameter semantics beyond the schema, only implying date-range filtering for 'trip' windows. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't significantly enhance parameter understanding.

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 clearly states the tool's purpose: 'Returns cities whose maple or ginkgo viewing windows overlap the trip, based on forecast peak dates.' This specifies the verb ('returns'), resource ('cities'), and scope ('maple or ginkgo viewing windows'), distinguishing it from siblings like koyo_spots (for park recommendations) and koyo_forecast (likely for general forecasts).

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 explicit guidance on when to use this tool ('when the user gives autumn travel dates and wants the best cities during that window') and when not to use it ('Do not use this for general climate questions or for exact park recommendations without dates'), with a clear alternative named ('use koyo_spots when the prefecture is already known'). This covers both inclusion and exclusion criteria effectively.

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