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hermoso-ai

Hermoso

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Finish video

finish_video

Post-process a rendered video by adding reviewer-style overlay pills and film grain, resulting in a finished ad with custom proof points and a natural camera look.

Instructions

Post-process an EXISTING rendered video (its served mp4 URL) with the proven direct-response 'reviewer' finish and/or a film-grain pass — no AI model, ~30s, a couple of credits. pills=true composites a header pill (e.g. '10/10 would buy again'), a brand-accent sub-pill, and 3-4 green-check proof pills cascading in on the beat (YOU author the copy: header ≤40 chars, sub ≤34, each point ≤44 — concrete real benefits, never fabricated stats). grain=true applies a subtle camera-grain finish that makes photoreal AI renders look phone-shot ('less AI') — works alone or with pills. Returns a NEW video; the original is untouched.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
subNoaccent sub-pill copy, ≤34 chars (usually the product/brand)
grainNodefault false — anti-AI film-grain finish
pillsNodefault true — set false for a grain-only pass
accentNobrand accent hex for the sub-pill
headerNoheader pill copy, ≤40 chars (required when pills is on)
pointsNo3-4 proof points, ≤44 chars each
videoUrlYesthe served URL of the video to finish (from a previous render/job)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rawNothe raw job result payload (e.g. images[] for carousel template ads)
urlNothe served URL of the finished media (absent/null while still rendering)
jobIdNothe render job id — poll get_job with this id to resume or inspect
modelNothe product-facing label of the model that rendered it
stillRenderingNotrue when the render is still in progress — keep polling get_job with jobId
Behavior4/5

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

With no annotations providing hints, the description carries the full burden. It discloses that the tool returns a new video, leaves the original untouched, takes ~30s, costs a couple of credits, and uses no AI model. It describes the effects of pills and grain precisely, though it could mention error handling for missing parameters.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-organized paragraph. It starts with the overall purpose, then details pills and grain separately, and ends with a note on return value. Every sentence is informative and concise, with no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (7 parameters, 1 required) and the existence of an output schema, the description is thorough. It covers the main behaviors, constraints, and return value. However, it could be more explicit about default behaviors (e.g., pills defaults to true) and handling of invalid parameter combinations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds significant value by explaining character limits for pills (header ≤40, sub ≤34, points ≤44), requiring concrete benefits, and describing the interplay between pills and grain. This goes beyond the schema's simple type/description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: post-processing an existing rendered video with pills (testimonial overlays) and/or film grain. It specifies the verb 'post-process', the resource 'rendered video', and distinguishes from siblings like 'render_ad' or 'stitch_video' by focusing on finishing touches.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains when to use this tool (after rendering) and provides detailed usage for pills and grain modes, including character limits. While it doesn't explicitly state when not to use it, the context from sibling tools implies alternatives for other post-processing tasks.

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

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