ffmpeg__apply_color_lut

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 discloses the return format ('Returns JSON in stdout with output_path and lut_path') and the underlying ffmpeg filter, which is useful. However, it does not mention potential side effects like file overwriting or dependency on ffmpeg, leaving some behavioral uncertainty.

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 concise and well-structured, leading with a clear one-sentence purpose followed by @remarks and @param entries. Every line adds necessary information without waste.

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?

Given the tool's moderate complexity, the description covers the operation, filter, return format, and all parameters. There is no output schema, so the explicit mention of JSON output is helpful. Minor gaps like file overwrite behavior prevent a perfect score.

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%, so the description's @param lines fully compensate by explaining each parameter's purpose (e.g., 'input_path Source video path', 'lut_path Path to a .cube format LUT file'). It also clarifies the default for __mcp_response_mode, adding value beyond the schema's enum.

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 opens with a specific verb and resource: 'Apply a .cube format 3D color Look-Up Table (LUT) to a video for color grading.' This clearly identifies the tool's function and differentiates it from sibling ffmpeg tools like add_fade or mix_audio_tracks.

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?

The description provides clear context by specifying the use case ('for color grading') and mentions the underlying filter ('uses the ffmpeg lut3d filter'). While it doesn't explicitly state when not to use the tool or list alternatives, the context is sufficient for an agent to select it appropriately among sibling tools.

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