japan-seasons-mcp

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

Annotations indicate read-only and idempotent operations, which the description doesn't contradict. The description adds valuable context beyond annotations: it specifies the data source (Japan Meteorological Corporation), the scope (48 observation cities), and the types of data returned (forecast bloom dates, full-bloom dates, etc.). However, it doesn't mention potential limitations like rate limits or authentication needs.

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 tool's purpose and usage guidelines, followed by specific exclusions. Every sentence serves a clear purpose: the first defines scope and data, the second provides usage rules. There is no wasted text, making it highly 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 (1 parameter, no output schema), the description is complete. It covers purpose, usage guidelines, data scope, and exclusions. With annotations handling safety (read-only, idempotent) and the schema detailing the parameter, the description fills all necessary gaps without redundancy.

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?

The input schema has 100% description coverage for its single parameter, so the baseline is 3. The description adds value by clarifying the tool's behavior when the parameter is omitted (returns all observation cities) and reinforcing the filtering logic, though it doesn't provide additional syntax details beyond the schema.

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: returning cherry blossom forecast data including bloom dates, full-bloom dates, observed dates, historical averages, and status for 48 observation cities. It uses specific verbs ('returns', 'do not use') and distinguishes itself from sibling tools like sakura_spots, which handles specific parks or temples.

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 (for cherry blossom timing, peak bloom, sakura status, or city comparisons) and when not to use it (for specific parks or temples, directing to sakura_spots instead). It provides clear alternatives and exclusions, making it easy for an agent to choose correctly.

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