japan-seasons-mcp

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

Annotations already declare readOnlyHint=true and idempotentHint=true, indicating safe, repeatable operations. The description adds valuable context beyond annotations: it specifies the forecast source (Japan Meteorological Agency), the time horizon (next 3 days), the granularity (6-hour rain probabilities), and the scope (one city). However, it doesn't mention rate limits, authentication needs, or error handling, which keeps it from a perfect score.

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 efficiently structured in two sentences: the first covers purpose and usage guidelines, the second clarifies exclusions and alternatives. Every phrase adds value without redundancy, and key information is front-loaded (e.g., 'short-range weather' and 'next 3 days').

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 (weather forecasting with specific parameters), rich annotations (readOnlyHint, idempotentHint), and high schema coverage, the description is largely complete. It explains the tool's scope, use cases, and limitations relative to siblings. However, without an output schema, it could benefit from more detail on return format (e.g., structure of forecast data), though the description does list the types of data returned.

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 the city parameter fully documented in the input schema (including a comprehensive list of supported cities and matching behavior). The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline of 3 where the schema does the heavy lifting.

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: it returns the next 3 days of Japan Meteorological Agency forecast text, temperatures, and 6-hour rain probabilities for one supported city. It uses specific verbs ('returns') and resources ('forecast text, temperatures, rain probabilities'), and explicitly distinguishes itself from sibling tools like sakura_forecast and koyo_forecast by stating it's for short-range weather, not seasonal bloom timing.

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 short-range weather could change the recommendation, especially for sakura petal fall, rain risk, or packing advice') and when not to use it ('Do not use this for seasonal bloom timing months in advance'). It names specific alternative tools ('use the sakura or koyo forecast tools for that'), making it clear when to choose this over siblings.

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