japan-seasons-mcp

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

Annotations already indicate read-only and idempotent operations, which the description doesn't contradict. The description adds valuable behavioral context beyond annotations: it specifies the data source (Japan Meteorological Corporation), clarifies the temporal scope (October-December), and describes the return content (city-level dates, forecast maps, regional commentary). This provides useful operational context that annotations alone don't cover.

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 when to use the tool with specific examples, the second describes what it returns and provides clear exclusion guidance. Every sentence serves a distinct purpose with zero wasted words, making it easy to parse and understand quickly.

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?

For a read-only forecast tool with good annotations and comprehensive schema documentation, the description provides complete contextual information. It covers use cases, exclusions, data source, temporal scope, and return content. While there's no output schema, the description adequately explains what the tool returns, making it sufficiently complete for the tool's complexity.

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?

With 100% schema description coverage, the schema already documents both parameters thoroughly. The description doesn't add significant parameter semantics beyond what's in the schema - it mentions region filtering and tree types but doesn't provide additional context about parameter usage or behavior. The baseline of 3 is appropriate when 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 provides autumn leaves forecasts for Japan, returning city-level maple and ginkgo forecast dates, maps, and regional commentary. It distinguishes itself from siblings by specifying it's for forecasts (not specific spots) and explicitly naming koyo_spots as the alternative for location-specific queries.

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 when asking about peak timing, city comparisons, or national overviews for October-December. It also gives clear exclusions: do not use for specific temples/gardens/GPS-tagged locations, and directs users to koyo_spots for those cases. This covers both when-to-use and when-not-to-use scenarios with named alternatives.

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