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by io.github.hermoso-ai
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Multiply an ad

multiply_ad

MULTIPLY a winning video ad into N variants: each gets a NEW character, outfit, location and/or objects while the cut, the camera motion, the pacing and the ORIGINAL AUDIO stay exactly as they were (that is what made the ad work), and any burned-in captions are removed. Pass the source video URL (a previous render, a job result, list_library, or the top performer from post_performance / meta_insights). HOW: the source video itself DRIVES each variant (motion transfer from one image-edited opening frame), so every variant comes back the SAME LENGTH as the source with the same cut, the same performance and the original audio — only the person, outfit, set and props change. Sources up to 30 seconds work as they are; longer ones are refused for free with the way out (trim it first: post_edit with ops [{op:'trim', start:0, end:30}] — clip_video is the AI highlight clipper, not a trim). Returns the plan and ONE JOB PER VARIANT — call get_job on each until it reports done; do not describe a variant before its URL arrives. Cost is quoted per variant in the reply (use dryRun:true to see the plan and the quote without rendering). Regions: pass regions:['Berlin','Tokyo'] to restyle variants per market; translation is a separate, explicit step — dub_video on a finished variant.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
axesNowhich axes to vary (default: all four)
countNohow many variants, 1-12 (default 6)
notesNoanything the variants must respect, e.g. "keep it women 25-40", "no gyms"
videoYesthe source video URL
dryRunNotrue = return the plan and the quote, render nothing
regionsNomarkets to restyle for, one or more variants each, e.g. ["Berlin","Tokyo","São Paulo"] — visuals only; audio is never translated here

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations only carry basic flags (readOnly false, destructive false), but the description discloses async job-per-variant behavior, the need to poll get_job, cost quoting, dryRun behavior, same-length output, caption removal, and refusal of long sources. This is far beyond what annotations provide and sets accurate expectations.

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

Conciseness3/5

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

The description is rich and front-loads the core purpose, but it repeats the preservation guarantees ('cut, camera motion, pacing, original audio' then 'same cut, same performance, original audio') and uses caps-heavy editorial emphasis. Each section is informative, yet a few sentences could be merged without losing meaning.

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

Completeness5/5

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

Despite having no output schema, the description covers the full call lifecycle: source selection, length constraint, job creation, polling via get_job, dryRun, per-variant cost, regions, and the adjacent translation step. An agent has everything needed to invoke the tool and handle its asynchronous response.

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 the baseline is 3. The description adds extra meaning for video (accepted sources, 30-second cap), axes (what changes), regions (visual-only restyling per market), and dryRun (plan and quote without rendering), though count and notes are left to the schema.

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?

States a specific action (MULTIPLY into N variants), the resource (a winning video ad), and the exact dimensions of variation (character, outfit, location, objects) while preserving cut, camera motion, pacing, and audio. This makes the tool's purpose unmistakable and clearly distinct from generic render/edit tools.

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

Usage Guidelines5/5

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

Explicitly tells the agent what source URLs are acceptable, imposes a 30-second source limit, and prescribes post_edit with a trim op for longer sources while warning that clip_video is not a trimmer. It also routes translation to dub_video and dryRun to previewing cost, which are clear when-to/alternatives signals.

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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