apply_lut_grade

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

No annotations are provided, so the description carries the full burden. It does disclose useful behavior: the default output filename pattern '{name}_graded.{ext}', the intensity semantics with endpoints spelled out, and the shape of the return value. However, for a file-writing operation it never says whether an existing output file is overwritten, whether the original input is left untouched, or what happens on invalid LUT data — meaningful gaps for a mutation 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?

The lead sentence is front-loaded and the Args/Returns structure is scannable with zero filler. The Returns section partially duplicates the output schema, but it is short enough not to be a burden.

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 four-parameter file-processing tool with an output schema present, the description covers all parameters and the return shape adequately. The remaining omissions — overwrite policy, error conditions, supported input codecs — are the only things standing between it and full completeness.

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%, so the description must compensate, and it largely does: it explains input_path ('full file path'), lut_path's accepted extensions, output_path's default naming pattern, and defines intensity's 0.0–1.0 range with concrete endpoint meanings ('original' vs '100% LUT grade'). Only minor gaps remain, such as validation behavior for out-of-range intensity values.

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 first sentence names a specific verb and resource ('Apply professional 3D Look-Up Table (.cube or .3dl) color grading to a video') and even states the supported file formats. The tool's function is unmistakable and cannot be confused with the sibling tools (remove_silence, smart_crop_vertical, extract_audio_and_metadata, generate_capcut_draft).

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?

Usage is implied by the tool's nature — an agent needing to apply a LUT grade would pick it — but the description never states when to use it, when not to, or what alternatives exist for color grading (e.g., other LUT-related tools). It also omits prerequisites such as the required video formats or whether ffmpeg-like dependencies must be present.

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