forecast_land_price_trend

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the description's additional context about methods (linear regression, moving average) and outputs adds moderate value. However, it lacks details on data sources, assumptions, or limitations, which would improve transparency.

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 with two English sentences and a Japanese translation. It is front-loaded but slightly redundant due to the duplicated Japanese section. Overall, it is 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?

For a forecasting tool with 7 parameters and no output schema, the description provides a basic understanding but misses details like output format, range of prefectures, and methodological assumptions. It is adequate but not fully comprehensive.

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 coverage is 86%, with each parameter having a description. The tool's description does not add meaning beyond the schema, meeting baseline expectations but not exceeding them.

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 function: forecasting land price trends using linear regression and moving average. It specifies outputs (CAGR, confidence interval, investment signal) and scope (10 prefectures), effectively differentiating it from siblings like get_real_estate_macro_snapshot.

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 implies usage for land price forecasting but fails to provide explicit when-to-use guidance, exclusions, or comparisons with semantically similar sibling tools such as compare_prefectures or drill_down_local_analysis.

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