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

Annotations already declare this as a read-only, idempotent, non-destructive, closed-world operation. The description adds useful behavioral context by clarifying that the result is conditional consistency, not image authenticity, which is critical for interpreting the output correctly.

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 two tightly written sentences with no wasted words. It front-loads the core action and ends with an important limitation, making it easy to scan.

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 complex tool with 10 parameters, nested objects, no output schema, and no schema parameter descriptions, the definition is too sparse. It gives a high-level method and one output caveat but lacks enough detail to call the tool confidently or understand the full return semantics.

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 0% across 10 parameters, so the description carries the full burden. It only partially clarifies inputs, mentioning 4–16 control points and Monte Carlo uncertainty, but leaves date_utc, latitude, longitude, shadow, second_shadow, seed, samples, pixel_sigma, and time_sigma_minutes unexplained.

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 states a specific verb and resource: 'Check measured shadows' using NOAA solar position, homography, and Monte Carlo uncertainty. It is clear what the tool does, but it does not explicitly distinguish itself from closely related siblings such as check_shadow_track.

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?

There is no explicit guidance on when to use this tool versus alternatives, nor any when-not-to-use conditions. The description implies a use case by naming 'measured shadows', but an agent still has to infer routing from the tool name and siblings.

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