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video_add_texts

Add multiple text overlays to a video in a single FFmpeg pass. Automatically distributes texts vertically to avoid overlap.

Instructions

Overlay multiple text elements on a video in a single FFmpeg pass.

Automatically detects overlapping text and distributes vertically stacked texts when they share the same named position.

Args: input_path: Absolute path to the input video. texts: List of text overlay dicts. Each dict may contain: - text (str, required) - position (str|dict, default "center") - font (str, optional) - size (int, default 48) - color (str, default "white") - shadow (bool, default True) - start_time (float, optional) - duration (float, optional) output_path: Where to save the output. Auto-generated if omitted. crf: Override CRF value (0-51, lower = better quality). Default 23. preset: Override FFmpeg encoding preset (ultrafast, fast, medium, slow, veryslow). auto_layout: Automatically distribute vertically stacked texts at the same named position. Default True.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
input_pathYes
textsYes
output_pathNo
crfNo
presetNo
auto_layoutNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations, the description carries the burden. It discloses automatic overlap detection and vertical distribution of texts at the same named position. It also mentions FFmpeg pass. However, it does not detail return value structure or performance implications, though output schema exists.

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 front-loaded with purpose, followed by a structured args list. It is slightly verbose with detailed parameter descriptions but remains organized and clear. Could be more compact without losing essential information.

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?

Given the complexity (6 parameters, nested text objects), the description covers all parameters and auto-layout behavior. Output schema exists, so return values are not needed. The description is sufficient for understanding tool operation and invocation.

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

Parameters5/5

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

Schema coverage is 0%, but the description provides explicit details for all 6 parameters, including nested structure for 'texts' dict items (text, position, font, size, etc.), and parameter defaults (crf=23, auto_layout=True). This fully compensates for the low schema coverage.

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: 'Overlay multiple text elements on a video in a single FFmpeg pass.' It uses a specific verb and resource, and distinguishes from the sibling tool 'video_add_text' which adds a single text.

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

Usage Guidelines3/5

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

The description implies usage for adding multiple texts with automatic layout, but does not explicitly state when to use versus alternatives like video_subtitles or video_text_animated. No when-not or exclusion criteria provided.

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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