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 behavioral context beyond annotations: it explains the data sources (JMC reporter observations vs. JMC bloom-meter estimates), freshness criteria (within 48 hours), status states (pre-bloom through hazakura/green leaves), and what gets returned (spot details plus prefecture summary). No contradictions with annotations exist.

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 with two sentences: the first states the purpose and key details, and the second provides usage exclusions and alternatives. Every sentence adds value without redundancy, making it front-loaded and appropriately sized for the tool's complexity.

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 (one parameter, no output schema, but detailed behavioral context), the description is largely complete. It covers purpose, usage guidelines, data sources, and return content. A minor gap is the lack of explicit output format details, but with annotations providing safety context, this is acceptable.

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 single parameter 'prefecture' well-documented in the schema. The description adds minimal parameter semantics beyond the schema, only implying the tool returns one prefecture at a time. This meets the baseline of 3 since the schema already provides comprehensive parameter information.

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: to provide exact cherry blossom viewing spots with current status and GPS coordinates for a specific prefecture. It distinguishes from siblings by specifying this is for spot-level details within a prefecture, not nationwide timing comparisons or date matching, which are handled by other tools like sakura_forecast or sakura_best_dates.

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 explicitly states when to use this tool ('when the user already knows the prefecture') and when not to use it ('Do not use this for nationwide timing comparisons or date matching'), while naming specific alternatives ('use sakura_forecast or sakura_best_dates first'). This provides clear guidance on context and tool selection.

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