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 does not contradict. The description adds valuable context beyond annotations by specifying the return content (forecast comment, map links, spot details with percentages and dates) and clarifying the scope (Kawazu-zakura, early blossoms, Izu Peninsula). However, it does not mention rate limits or authentication needs, though annotations cover safety aspects adequately.

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 concise and well-structured: it starts with usage guidelines, specifies the return content, and ends with exclusions. Every sentence adds value without redundancy, making it efficient and front-loaded for quick comprehension by an AI agent.

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 complexity (forecast data with spot details), annotations cover safety (read-only, idempotent), and the description provides comprehensive usage guidelines and return content. Although there is no output schema, the description details what is returned (forecast comment, map links, spot data), making it complete enough for an AI agent to understand and invoke the tool correctly.

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 documents both parameters thoroughly. The description does not add parameter-specific semantics, but it implies usage context (e.g., 'when the user only needs the overall forecast summary' relates to include_spots). Since schema coverage is high, the baseline is 3, but the description's contextual hints slightly enhance understanding, warranting a score of 4.

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 Japan Meteorological Corporation forecast comment, forecast map links, and Kawazu cherry spots with specific data (bloom percentages, forecast dates, coordinates). It explicitly distinguishes from sibling tools by specifying 'Do not use this for standard Somei-Yoshino sakura elsewhere in Japan,' making it highly specific and differentiated.

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 guidelines: 'Use this for January-February cherry blossom requests or when the user specifically asks about Kawazu-zakura, early blossoms, or the Izu Peninsula.' It also gives clear exclusions: 'Do not use this for standard Somei-Yoshino sakura elsewhere in Japan,' effectively guiding when to use this tool versus alternatives like sakura_forecast or sakura_spots.

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