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Generate image with AI

generate_image
Destructive

Generate image(s) with AI. Requires the project's AI image generation to be enabled (check get_project context.imageGeneration.enabled or get_imagegen_quota first); if generation is disabled, the monthly quota is exhausted, or too many jobs are running concurrently, the tool returns a clear error. START-ONLY: returns { status: 'pending', jobId } immediately; poll get_image_job(jobId) to retrieve the finished assets, then set an asset url as a section media.src. Use this for custom, branded, or illustrative imagery; prefer search_stock_images + upload_asset for real-world photos.

Input Schema

NameRequiredDescriptionDefault
countNoHow many images to generate (1-4)
promptYesDescription of the image to generate (max 2000 chars)
qualityNoGeneration quality
projectIdYesThe project ID
aspectRatioOverrideNoAspect ratio override

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations only reveal readOnly=false, destructive=true, openWorld=true; the description adds the behavior those hints do not: quota consumption, concurrency limits, clear-error behavior when disabled/exhausted, and the exact immediate return shape { status: 'pending', jobId } with no output schema to convey it. This is substantial context beyond structured data.

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?

Front-loaded with the purpose, then prerequisites, return contract, and routing guidance in a logical order; each clause earns its place. It is dense and parenthetical-heavy, which costs a little readability, but there is no filler.

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

Completeness5/5

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

For a 5-parameter, async, side-effecting tool with no output schema, the description covers prerequisites, error conditions, the immediate return contract, and the required polling follow-up. Nothing an agent needs to invoke and complete the workflow correctly is missing.

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 description coverage is 100%, so every parameter (count, prompt, quality, projectId, aspectRatioOverride) is already documented in the schema; the description adds no per-parameter syntax, format, or constraint detail. Baseline 3 is appropriate when the schema carries the load.

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?

States a specific verb+resource ('Generate image(s) with AI') and immediately clarifies the async, start-only nature of the operation. It distinguishes itself from siblings by naming search_stock_images + upload_asset as the alternative for real-world photos, so an agent can route without opening any schema.

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

Usage Guidelines5/5

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

Explicit when-to-use ('custom, branded, or illustrative imagery'), explicit when-not ('real-world photos' → search_stock_images + upload_asset), plus prerequisites to check first (get_project context.imageGeneration.enabled, get_imagegen_quota). The follow-up workflow (poll get_image_job, set url as section media.src) is also spelled out.

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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