japan-seasons-mcp

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

Annotations indicate readOnlyHint=true and idempotentHint=true, which the description doesn't contradict. The description adds valuable behavioral context beyond annotations: it specifies that returns include curated spots with peak windows, official URLs, notes, and GPS coordinates. However, it doesn't mention potential limitations like rate limits or data freshness, which could be useful for a read-only 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 perfectly concise and well-structured: two sentences that efficiently convey purpose, scope, return format, and usage guidelines. Every word earns its place with no redundancy or wasted space. The information is front-loaded with the core purpose immediately clear.

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 (3 optional parameters, read-only operation) and excellent annotations, the description provides strong contextual completeness. It clearly explains what the tool returns and when to use it versus alternatives. The main gap is the lack of an output schema, but the description partially compensates by listing what information will be returned (spots with peak windows, URLs, notes, GPS coordinates).

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%, so the schema already fully documents all three parameters. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions flower types and filtering by month, but these are already covered in the parameter descriptions. Baseline 3 is appropriate when 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: to return curated flower spots for specific non-sakura flower types with detailed information like peak windows, URLs, notes, and GPS coordinates. It explicitly distinguishes this tool from its siblings (sakura and koyo tools) by specifying it's for 'non-sakura flower trips' and listing examples like plum, wisteria, and hydrangea.

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 usage guidance: 'Use this for non-sakura flower trips... Do not use this for cherry blossom or autumn leaves timing; use the sakura or koyo tools for those live forecasts.' It clearly defines when to use this tool versus alternatives, naming specific sibling tools for different use cases.

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