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generate_video

Start rendering an AI presenter video from a script, avatar and voice. Returns a job id immediately; poll get_video_status until it completes. Rendering is metered and spends the user's video allowance — confirm the script first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scriptYesWhat the presenter says (1–5000 characters).
captionsNoBurn in captions (default true).
providerNoVideo provider (default heygen, currently the only one).
voice_idYesVoice id — from list_video_voices.
avatar_idYesAvatar id — from list_video_avatars.
aspect_ratioYesVideo shape.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations cover the safety profile (readOnlyHint=false, idempotentHint=false, destructiveHint=false), and the description adds genuinely new context: it returns a job id immediately (async, not synchronous), rendering is metered, and it spends the user's video allowance. These behavioral traits are not derivable from the annotations, and the cost warning is especially useful.

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?

Three tight sentences that are front-loaded with the action, then the return/poll behavior, then the cost caveat. No filler and every sentence carries necessary 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?

With no output schema, the description fills the gap by explaining it returns a job id and how to poll for completion, plus the cost implication. An agent has everything needed to invoke it correctly and handle the result.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all six parameters including defaults and the enum. The description merely restates 'script, avatar and voice' without adding format or syntax detail, so the baseline 3 applies.

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 states a specific verb and resource ('Start rendering an AI presenter video') and names the three key inputs (script, avatar, voice). This clearly distinguishes it from siblings like generate_image or create_video_script without needing to compare schemas.

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?

It names the follow-up tool explicitly ('poll get_video_status until it completes') and gives a prerequisite ('confirm the script first'). It doesn't compare against alternatives like create_video_script, but the when-to-use and next-step guidance is clear.

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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