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

Beyond annotations, it discloses the asynchronous job model (job_id returned, polling required), non-cancellable jobs, GPU inference, area-dependent runtime, and GeoJSON output. These are non-obvious behavioral details that materially affect how an agent should use the 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?

Well-organized with labeled bullet sections for input, area limits, processing, output, and lifecycle. Every section carries operational value and the structure makes the flow easy to follow despite the length.

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 an asynchronous job tool with no output schema, the description covers input requirements, area constraints, processing behavior, output type, job lifecycle, and prerequisite preview/approval step. Nothing critical for correct invocation is missing.

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%, and the schema already documents each parameter including WKT format, date range, and sentinel-1 constraint. The description reinforces these constraints but does not add meaningfully new parameter semantics beyond the schema, so the baseline 3 applies.

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?

States a specific verb (検出) and resource (消失建物), and the method (Sentinel-1 SAR前後比較) makes the tool distinct. It is clearly differentiated from siblings like analyze_newbuilding and analyze_oilslick by domain and processing approach.

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?

Provides strong operational guidance: use preview_analysis before execution, poll check_job_status, get results via get_job_result, and split areas over 30000 km². It does not explicitly name sibling alternatives for when-not-to-use, so it stops short of a 5.

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