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generate_video_batch

Create a video batch from a list of topics or headlines. Submit a channel ID and topics to generate themed videos and queue them for automated production.

Instructions

Generate a batch of videos in yzturkey.com for a list of topics or titles.

Creates the batch and themes, queues them for video production and scene generation.

Args: channel_id: Channel UUID or slug. topics: List of video topics or headlines (e.g. ['Dünyanın En Derin Çukuru', 'Mariana Çukurundaki Gizemler']). batch_id: Optional existing series/batch UUID to append these videos to. batch_name: Name for the batch if creating a new one (e.g. 'Derin Denizler #1'). module_type: Video engine: 'veo', 'faceless', 'quiz', 'map'. Default is 'veo'. production_mode: Optional production mode override.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicsYes
batch_idNo
batch_nameNo
channel_idYes
module_typeNoveo
production_modeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the side-effect disclosure burden and does so by explicitly saying it creates the batch/themes and queues them for video production and scene generation. It does not cover permissions or reversibility, but the core behavioral contract is far from a bare 'generate'.

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 compact and front-loaded: a one-sentence purpose, a one-sentence side effect, then a clean Args list. Every line adds useful information with no boilerplate or repetition of the title.

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

Completeness4/5

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

For a 6-parameter mutating tool with no annotations, the description is mostly complete: required arguments, optional append behavior, and async queueing are all covered, and an output schema exists to document returns. The vague production_mode override and lack of relation to duplicate-checking are the main remaining gaps.

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?

The input schema has 0% description coverage, so the Args block is essential and it covers all six parameters with types, examples, and allowed values (e.g., module_type 'veo'/'faceless'/'quiz'/'map', channel_id UUID or slug). Even optional parameters get semantic context, which fully compensates for the empty 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?

The description states a specific verb+resource: generate a batch of videos on yzturkey.com from a list of topics, and clarifies that it creates the batch/themes and queues production. This clearly separates it from channel and series management siblings.

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 parameter notes imply usage patterns (append via batch_id, create new via batch_name), but the description never explicitly states when to choose this tool over related tools such as check_duplicate_idea or create_series. There is no when-not-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.