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Render Manim Video

render_video

Render multiple Manim scenes concurrently and concatenate them into a single video with AI voiceover. Auto-corrects common issues such as wrong TTS service or MathTex.

Instructions

Render one or more Manim scenes in parallel, concatenate them, and return one combined video inline. Each scene has complete Python code with voiceover baked in via manim-voiceover. Scenes render concurrently — voice is generated and synced during rendering automatically. The server auto-fixes common issues: wrong TTS service → ElevenLabs, CYAN → TEAL, MathTex → Text.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scenesYesArray of scenes to render in parallel. Order matters for final video.
qualityNoVideo quality: l=480p (default), m=720p, h=1080pl

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
scenesYes
overviewYes
videoUriYes
durationSecondsYes
Behavior4/5

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

With no annotations, the description takes full responsibility for disclosing behavior. It reveals that scenes render concurrently, voiceover is auto-synced, and the server auto-fixes common issues like wrong TTS, color, and MathTex usage. This goes beyond basic functionality and helps set expectations, though it doesn't cover all potential failure modes or side effects.

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 four sentences, each serving a purpose: main action, scene requirements, concurrency behavior, and auto-fix details. There is no redundancy or fluff. It is front-loaded with the primary action and remains skimmable.

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 tool's moderate complexity (2 params, one enum, nested scene objects) and the presence of an output schema, the description covers all necessary behavioral context: parallel rendering, auto-fixing, and voiceover handling. It does not need to explain return values because an output schema exists, and the input schema already documents parameter constraints.

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 meaning beyond the schema by explaining that each scene's code must have voiceover baked in via manim-voiceover, and that scenes render concurrently. This helps the agent understand the intent behind the code parameter and the parallel nature of the scenes array.

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 specific verb+resource+outcome: 'Render one or more Manim scenes in parallel, concatenate them, and return one combined video inline.' This clearly distinguishes it from the sibling tool show_demo_video, which presumably displays a pre-made demo rather than rendering new content.

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 implies usage: you use this tool to render and combine Manim scenes into a single video. It doesn't explicitly mention alternatives or exclusions, but the clear purpose and the existence of a sibling tool provide enough context for an agent to decide. Since it lacks explicit 'when not to use' guidance, it misses the top score.

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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