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Set generation defaults

flow_set_generation_defaults

Configure per-project defaults for image and video aspect, count, model, and credit spend approval to control Google Flow generation behavior.

Instructions

Model defaults the agent uses (image aspect/count/model, video model/aspect/count) and whether it must ask before spending credits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
imageNo
videoNo
project_idYesFlow project id (from flow_list_projects).
credit_spend_approvalNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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 adds useful context on the credit-approval stance ("whether it must ask before spending credits"), but for a mutation it never states whether existing defaults are overwritten or merged, whether partial updates are valid, or what permissions are required.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single sentence with no filler, and the most consequential item (credit spend approval) is placed last for emphasis. The phrasing "Model defaults the agent uses" is slightly indirect but wastes no words.

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 mutation with nested objects, no annotations, no output schema, and 25% schema coverage, the description is thin: it omits project_id, says nothing about update semantics (replace vs merge), and gives no guidance on the required video fields. An agent could invoke it but would be guessing at behavior.

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 only 25%, so the description must compensate by enumerating image (aspect/count/model), video (model/aspect/count), and clarifying credit_spend_approval. That enumeration is largely a restatement of the nested schema fields, and project_id plus the enum meanings are not elaborated, so it only partially closes the gap.

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 names the resource precisely (image/video generation defaults plus credit-approval policy) and its effect is unambiguous relative to generation siblings like flow_generate_image. It lacks a leading verb and never explicitly contrasts itself with those siblings, so it falls short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use or when-not-to-use guidance. The description lists configurable fields but never says to call this before generating media, or how it interacts with the sibling generation tools, leaving the agent to infer the workflow.

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