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

With no annotations, the description fully carries the burden and does so well: it discloses the per-date return shape (clearest scene, cloud %, NDVI mean/median, three PNG products), the derived change + reliability fields, the failure mode (404 when no scene) with the significant commercial consequence that it is not charged, and the x402 payment price and rails. Cost, error behavior, and payment mechanics are exactly the traits an agent cannot get from structured fields.

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?

Front-loaded with the 'Use when' trigger, then organized into Input / Output / Error / Example / Price blocks — nothing is wasted. The telegraphic spec-sheet phrasing ('Output: JSON per date: …') is dense but scannable; a slightly less compressed output sentence would read better.

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?

With no output schema, 7 parameters, and no annotations, the description covers the essentials an agent needs: trigger, inputs, return payload, error semantics, and price. Remaining gaps are minor — what 'reliability' measures and the two optional tuning params (size, window_days) are never mentioned.

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 100%, so the baseline is 3, but the description adds genuinely new constraints not in the schema: the radius_m operating range of 200–10000 m (schema only gives the default) and a fully worked query example for lat/lon/before/after. It omits size and window_days entirely, and drops the schema's '2017-01-01 or later' floor for `before`.

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+resource pair — checking what changed at a place between two dates — and enumerates the concrete use cases (construction, deforestation, flooding, crops). It implicitly distinguishes itself from satellite_image (single scene) and satellite_trend (time series) via the 'two dates before/after' framing, but never names those siblings explicitly.

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?

Opens with 'Use when checking what changed…' which is a clear positive selection rule, and adds hard preconditions (dates 20+ days apart, radius 200–10000). It gives no exclusions or explicit pointers to satellite_image / satellite_trend for single-date or long-series needs, so the agent must infer routing.

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