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flow_use_grid_architect

Open Grid Architect in Google Flow to configure theme, shot prompts, engine, ratio, and visual logic for batch shot generation in brand campaigns.

Instructions

Open Grid Architect in Google Flow, fill theme prompt, shot prompts, engine, ratio, and visual logic settings. Supports batch shot generation for brand campaigns.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
theme_promptYesOverall theme prompt for the grid.
shot_promptsNoArray of individual shot prompts for the grid.
engineNoEngine/model for the grid.Nano Banana 2
ratioNoAspect ratio for all shots.16:9
visual_logicNoVisual logic type: None, Colour Pop, Side by Side, etc.
referencesNoPaths to reference images.
project_nameNoName for the project (will reuse existing project with same campaign, or create new).
campaignNoCampaign identifier for project matching (e.g., "ete-2026", "nouvelle-collection").

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses that the tool opens a UI screen and fills settings, and mentions batch generation behavior, but it does not disclose prerequisites (e.g., needing an active Google Flow session), side effects like project creation/reuse, or what the output will be. This is more transparent than a minimal description but still lacks depth.

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 two sentences, front-loaded with the main action and settings, with no fluff. Every sentence conveys essential information, and it is appropriately sized for the tool's complexity.

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

Completeness3/5

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

Given the tool has 8 parameters, no output schema, and no annotations, the description provides an adequate overview but omits details on expected return values, prerequisites, and project handling behavior. The schema covers parameter details, but the description could be more complete about invocation context and outcomes.

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 coverage is 100%, so the baseline is 3. The description lists several parameter names and adds context for batch generation and campaigns, but it does not add substantial meaning beyond the schema's parameter descriptions, which are already detailed.

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 clearly states the tool opens Grid Architect in Google Flow and fills specific settings (theme prompt, shot prompts, engine, ratio, visual logic), with support for batch shot generation. It distinguishes itself from sibling tools like flow_generate_image by focusing on batch grid generation for brand campaigns.

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 phrase 'Supports batch shot generation for brand campaigns' provides clear context for when to use this tool (batch/brand campaigns). It does not explicitly state alternatives or when-not to use, but the batch context differentiates it from single-generation siblings like flow_generate_image.

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