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populate-template

Batch-create personalized compositions by duplicating a template for each data row and populating its layers with that row's values, handling text, footage, and properties.

Instructions

Duplicate a template composition once per row of a data table, populating each duplicate from that row's values - the batch/bulk version of manually editing one composition (personalized ads, product cards, name badges, anything repeated with different content). Each row can drive text content (with the same Arabic/RTL auto-detection as localize-comp), an image/video source (imported from a file path), or any layer/effect property value (reusing the same targeting scheme animate-to-audio/animate-from-data use). Layers nested inside precompositions are reachable via path, and each precomposition on that path is safely duplicated per row (never edited in place), reusing the same precomp-duplication logic localize-comp uses. Chain with render-aerender afterward, once per created composition, to batch-render every variant.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowsYesOne entry per output composition (max 500, one row becomes one duplicated composition). Each row is a flat {field: value} object; `bindings` below decides which fields drive which layers.
bindingsYesOne entry per layer to populate. Applied to every row.
compNameNoTemplate composition name (or the active comp if omitted).
compIndexNo1-based index among compositions, if compName is omitted.
namePatternNoName for each created composition, with {field} placeholders substituted from that row's values (e.g. '{name} ad'). Defaults to '<template name> <row number>', also used as the fallback if a placeholder's field is missing from a row.
Behavior4/5

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

With no annotations provided, the description fully carries the transparency burden. It discloses non-destructive behavior ('safely duplicated per row (never edited in place)'), RTL auto-detection, file import behavior, and precomposition duplication logic. This gives the agent a solid understanding of side effects and system interactions.

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

Conciseness4/5

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

The description is a single, information-dense paragraph. It is logically structured: purpose, capabilities, precomp behavior, and recommended follow-up action. Every clause adds value, with no filler. While it could be broken into bullet points, the flow is natural and efficient for the tool's complexity.

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?

The description covers all critical aspects: data binding for text, footage, and properties, handling of precompositions, safety guarantees, and integration with rendering. It also anticipates common use cases (personalized ads, product cards). Given the tool's complexity, nothing significant is missing, and the absence of an output schema is immaterial here.

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?

The schema already has 100% description coverage, so the description wisely avoids redundancy. Instead, it adds conceptual meaning by explaining the row-to-composition mapping, the kinds of values that can be driven (text/footage/property), and the reuse of targeting schemes from sibling tools. This enriches the agent's mental model without repeating schema details.

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 opens with a concrete, specific action: 'Duplicate a template composition once per row of a data table' – this is a clear verb+resource+scope. It further distinguishes itself from siblings by calling itself 'the batch/bulk version of manually editing one composition' and references sibling tools (localize-comp, animate-to-audio) for comparison, making the purpose unmistakable.

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 clearly implies when to use this tool (batch operations) versus alternatives by labeling it the 'batch/bulk version' and noting that it reuses logic from other tools. It also gives explicit chaining advice: 'Chain with render-aerender afterward'. It doesn't explicitly state when NOT to use it, but the context is strong enough for an agent to decide.

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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