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flow_plan_batch

Plan an image-to-video batch by validating scenes and parameters, calculating credit cost and SHA-256 plan hash, and returning a stateless plan to the client.

Instructions

Plan an I2V video batch statelessly. Validates scenes and parameters, computes credit cost and deterministic SHA-256 plan hash, and returns the plan state to the MCP client. Zero server-side persistence.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNo
modelNoomni_flash
aspectNo16:9
scenesYes
durationNo
backgroundNo
resolutionNo360p
estimated_credits_per_sceneNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.75.0

TDQS

A3.6/5.0
Behavior5/5

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

With no annotations, the description carries the full transparency burden and does well: it discloses stateless behavior, zero server-side persistence, validation, credit-cost computation, and a deterministic SHA-256 plan hash. This is meaningful behavioral context beyond the schema.

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?

Two dense sentences, front-loaded with the core purpose and followed by key behavioral traits. There is no filler or schema repetition.

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

Completeness2/5

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

For a tool with eight underdescribed parameters, no annotations, and a generic output schema, this description is not enough for an agent to construct valid scenes or know valid values for model/resolution/aspect. The high-level plan behavior is clear, but the operational details are missing.

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

Parameters2/5

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

Schema description coverage is 0%, so the description needed to explain parameters, but it only generically says it validates scenes and parameters. It adds no meaning for count, model, aspect, duration, background, resolution, or estimated_credits_per_scene.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies a specific action — planning an I2V video batch — and distinguishes it from submit/generate/poll siblings. It does not explicitly differentiate the closely named yenflow_plan_batch sibling, so it stops short of full sibling differentiation.

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 stateless/zero-persistence language implies this is a planning-only preview step, and returning a plan state suggests it should precede submission tools like flow_submit_scene. However, no explicit when-to-use or when-not-to-use guidance is given.

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