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Create several images at once with Google Flow

generate_images

Run multiple image requests in parallel across up to three Flow tabs, uploading shared attachments once and returning per-job results; ideal for batches like one image per scene.

Instructions

Runs several separate image requests in parallel (up to 3 Flow tabs at a time, each in its own agent session), about 3x faster than one by one. Use it for a set of different images, e.g. one per scene or one per reference frame. Files in attachments are uploaded once for the whole batch. Each job reports its own files or error. Free.

Input Schema

NameRequiredDescriptionDefault
jobsYes
parallelNoTabs at once (default 3)
project_idNoExisting project (flow_projects). Use it to keep working on the same piece.
new_sessionNoStart a fresh agent chat in the project (media kept, conversation memory reset)
confirm_rightsNoThe user confirmed they hold the rights to the attached files; Flow asks this on upload
project_purposeNoFor a new project: one line on what it is for
new_project_titleNoInstead: create a new project with this title, for a new idea or deliverable

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.6.0

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden and does well: it discloses the concurrency ceiling (3 tabs, each its own agent session), the ~3x speedup, that attachments are uploaded once per batch rather than per job, per-job success/failure reporting, and that the operation is free. It doesn't cover auth/sign-in prerequisites or what happens on partial batch failure beyond 'each job reports its own error'.

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?

Five short sentences, front-loaded with what it does and why it's faster, then usage, then upload/error semantics, ending with the cost-relevant 'Free.' Nothing is padded and every sentence carries distinct information.

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 7-parameter batch tool with no annotations and no output schema, the description covers speed, concurrency, upload behavior, and per-job results, which is most of what an agent needs. It leaves gaps on prerequisites (sign-in, credits) and the overall batch return shape, which matter given the absent output schema.

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 coverage is already 86%, so the baseline is 3, but the description adds real meaning the schema lacks — that attachments are uploaded once for the whole batch and that each job independently reports files or error, which clarifies the jobs array's semantics. The parallel and project_id parameters are not explained beyond what the schema already says.

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 opens with a specific verb and resource: it runs several separate image requests in parallel, capped at 3 Flow tabs, roughly 3x faster than serial generation. It implicitly distinguishes itself from the singular generate_image sibling via the 'several separate image requests' and 'one by one' framing, but never names the alternative, so the contrast is inferred rather than stated.

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 gives a concrete when-to-use rule: 'Use it for a set of different images, e.g. one per scene or one per reference frame,' which tells the agent the batch shape it fits. It stops short of stating when NOT to use it — e.g. that a single image should go to generate_image — leaving that selection to inference.

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