get_normalized_clips

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

With no annotations provided, the description carries the full burden and does so well by disclosing key behavioral traits: it runs tasks in parallel using ThreadPoolExecutor, includes a progress bar, automatically determines workers based on CPU cores, and skips clips with errors while continuing processing. This covers execution method, user feedback, resource management, and error handling without contradictions.

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 appropriately sized and front-loaded, starting with the core purpose, followed by structured sections for parameters, returns, and notes. Every sentence adds value, such as explaining defaults and behavioral notes, but it could be slightly more concise by integrating some parameter details into the initial summary.

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 complexity of a 6-parameter tool with no annotations or output schema, the description is largely complete: it explains the tool's purpose, all parameters, return values (sorted list of file paths), and key behaviors like parallel processing and error handling. However, it lacks details on output formats or error messages, which could enhance 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?

The schema description coverage is 0%, so the description must compensate, and it does by explaining all parameters beyond the schema. It adds meaning for each optional parameter (e.g., resolution as tuple, crf for quality control, preset for encoding speed vs. efficiency, max_workers for parallel threads), though it could provide more detail on valid ranges or formats for some parameters like 'preset'.

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 clearly states the tool's purpose with specific verbs ('normalize multiple video clips in parallel') and resources ('video clips'), distinguishing it from siblings like 'scale_video' or 'crop_video' by focusing on comprehensive normalization across resolution, frame rate, codec, and compression parameters rather than single transformations.

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 implies usage for batch normalization of video clips but does not explicitly state when to use this tool versus alternatives like 'scale_video' for resolution changes only or 'clip_video' for trimming. It mentions parallel processing and error handling, which provides some context, but lacks direct comparison or exclusion criteria for sibling tools.

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