video_fix_design_issues

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

No annotations provided, so description must fully disclose behavior. Only states it applies fixes, but doesn't mention whether operation is destructive, file size/quality impact, or required codec support. Missing key behavioral details.

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?

Description is concise and front-loaded with core purpose. Argument list is well-organized. No unnecessary words, but could benefit from brief bullet points for parameters.

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 video fixing tool, description covers basic operations but lacks details on output format, prerequisites, and how it differs from similar tools. Output schema exists but not shown; if it details return values, completeness is adequate. Still, some gaps remain.

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 description adds value by explaining input_path as absolute path and output_path as optional with auto-generation. However, no information on acceptable formats, constraints, or examples.

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 auto-fixes design issues like brightness, contrast, saturation, and audio levels. Differentiates from many video tools but does not explicitly contrast with specific siblings like video_ai_color_grade or video_normalize_audio.

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?

No guidance on when to use this tool versus alternatives (e.g., manual color grading or audio normalization). No when-not or prerequisite information provided.

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