data_pixel_sample

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

With no annotations, the description carries the behavioral burden. It discloses the sampling behavior, threshold semantics, and the optional full-frame numpyArray() read. It does not explicitly state read-only/unmodified effect, but for a pure sampling/statistics tool that behavior is logically inherent.

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 tight, front-loaded with the core purpose, and the parameter list is compact and readable. It is not excessively long, although the appended parameter documentation could be formatted more cleanly as a separate section.

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?

Output schema is absent, but the description names the expected luma stats and flags, which is sufficient for an agent to infer result shape. It could be more complete by describing the exact output format or a return key example, but the essential semantics are covered.

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 0%, and the description provides meaning for every parameter: path, grid (samples per axis), numpy (full-frame read), dark_below threshold, and solid_std threshold, including sensible defaults. This is far beyond the schema and gives an agent everything needed to fill parameters correctly.

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?

Description clearly states it computes luma statistics (mean/min/max/std) of a TOP with dark, solid, and clipped flags. The phrase 'is the output actually black?' distinctly identifies a concrete use case and distinguishes this pixel-sampling tool from sibling data_* tools.

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 clear contextual usage: it is described as the 'black output verifier', which tells the agent when to invoke it. It does not explicitly name alternatives or give exclusion criteria, but the use case is explicit enough for most callers.

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