gflow_generate_video

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

With no annotations provided, the description carries the full behavioral burden and does a solid job: it explains batch size via count, character resolution into referenceEntities, the pre-submit editor verification that aborts before spending credits, and the fact that the tool returns a local file path. It stops short of describing the wait parameter's blocking behavior or explicit credit consumption, but the coverage is still well above minimal.

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 dense but well-organized, with the core purpose front-loaded and each sentence adding concrete information. The parenthetical-heavy middle sections are somewhat hard to scan, but for an 18-parameter tool the length is justified and there is little wasted text.

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 complex tool with 18 parameters and no annotations, the description covers the most decision-relevant details: modes, model, aspect, duration, count, character references, ui_mode safety behavior, and return value. It remains incomplete because several optional parameters that could affect invocation, such as wait, project, output, initial_frame, and end_frame, are not addressed even though related sibling tools like gflow_list_projects exist.

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 input schema has 0% description coverage, so the description must compensate, and it adds real meaning for many parameters: mode values, aspect choices, model names, duration units, count as batch size, reference_images as one-off ingredients, and ui_mode behaviors. However, several parameters such as wait, project, project_name, output, initial_frame, end_frame, tools, and reference_entity_names receive no explanation, leaving a noticeable semantic gap.

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: 'Generate a video using Google Flow's Veo model.' It further distinguishes itself by enumerating video-specific modes (t2v, i2v, r2v) and aspects, making it clearly different from the sibling gflow_generate_image tool.

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 about mode options, model variants, and how to reference characters vs. pass one-off images, and it explicitly notes that ui_mode 'agentic' is not supported for video. However, it does not explicitly state when to choose this tool over gflow_generate_image or other siblings, so the usage guidance is mostly implied rather than stated as when-to-use/when-not-to-use.

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