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flow_generate

Queue AI video and image generation jobs in Google Flow through the extension. Returns a jobId without waiting for clips to render; use flow_wait to check results.

Instructions

Queue a generation in the live Google Flow through the extension. Returns jobId immediately WITHOUT waiting for the clips — wait with flow_wait. File paths (frames/characters) are absolute; the bridge reads them. Paths on another machine are unreachable: ask the user to copy files to the machine where the bridge runs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chainNoChaining for all items: every next clip starts from the last frame of the previous one
itemsYesGeneration items, one per clip
modelNoDefault model for all items
aspectNoDefault aspect ratio
folderNoSubfolder name for the results
lengthNoDefault length
prefixNoFile name prefix
resolutionNoDefault resolution

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.1.2

TDQS

A4.4/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full behavioral burden. It discloses asynchronous queuing (returns immediately), the reliance on the bridge to read absolute file paths, and the cross-machine limitation. Minor failure modes or side effects are not discussed, but core behavior is transparent.

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 sentences with no filler. The first sentence establishes purpose and immediacy; the second adds the path caveat and remediation. Each sentence earns its place and is front-loaded.

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?

Given the tool's complexity, the lack of annotations, and the absence of an output schema, the description covers the critical non-obvious behavior: async jobId return, file path assumptions, and the bridge machine constraint. Remaining parameter details are fully documented in the schema, so nothing essential is missing.

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 description coverage is 100%, which sets the baseline at 3. The description adds meaningful semantics for frames and characters: their paths are absolute, read by the bridge, and unusable from another machine. This goes beyond the schema's per-field descriptions.

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: 'Queue a generation in the live Google Flow through the extension.' It also explicitly contrasts itself with flow_wait by noting it returns jobId immediately and does not wait for clips, making sibling differentiation clear.

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 gives an explicit alternative ('wait with flow_wait') and a concrete when-not-to-use condition: file paths on another machine are unreachable, so the user must copy files to the bridge machine. It does not enumerate all sibling tools, but it covers the most important routing decision.

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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