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image_generate

Generate still images from text prompts, optionally guided by reference images for product or character consistency. Batch up to 10 images in a single call.

Instructions

For a user's image request, load the generating-images skill BEFORE calling this — it picks the right model and builds the prompt (this tool does neither, and calling it raw gives weak, inconsistent results). Generate one or many still images from text prompts, optionally guided by reference images (a product photo, a character, a style or composition to follow). Pass requests: ONE object per image (wrap even a single image — { requests: [ { prompt } ] }). Generate a batch of DIFFERENT images in a SINGLE call by adding more request objects (up to 10), each with its own prompt/model/aspect_ratio/resolution/reference_images; a single approval covers the whole batch. Each result carries a hosted image URL plus a local file path, or a structured error with a hint. Use for graphics, mockups, product/marketing visuals, logos, concept art, or to render a product or character from a supplied reference. Images are polled for you; a heavy image (large model / 4k / big batch) that runs long returns {status:"pending", ...} (a job handle, not an error) — pass that exact handle to job_status to retrieve it, and never re-submit a pending image. Set dry_run=true to preview the exact requests and cost without generating (no credits spent).Each entry in results is one of three things: finished media; a {status:"pending", ...} job handle to rejoin with job_status; or a failure carrying ok: false and an error. A failed entry is terminal — report its error and never poll or re-submit it. Read every entry rather than the top-level counters alone.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dry_runNoIf true, return the requests that would be sent (keys masked), make no API call.
requestsYesOne object per image (wrap even a single image); add more objects to batch different images in one call (up to 10).
Behavior5/5

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

With no annotations, the description carries the full burden and does so thoroughly. It explains polling behavior, pending job handles, terminal errors, result structures (URL/path/error), reference image constraints, and no silent resolution downgrades—far beyond basic operation details.

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?

The description is long but dense with actionable details; it is front-loaded with the most critical instruction (load skill first). Some redundancy exists (e.g., discussing result entries twice), but every sentence earns its place overall, so it loses only one point.

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?

Given the tool's complexity (batching, references, dry_run, pending, errors) and no output schema, this description is remarkably complete. It covers invocation, result types, failure handling, polling, and cost preview, leaving no obvious gaps for an agent to misuse the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although schema coverage is 100%, the description adds substantial meaning: it explains the nested request structure (wrap single image, batch up to 10), guides model selection, elaborates on reference_images use cases, explains dry_run's behavior, and clarifies resolution/aspect ratio error handling—all beyond the schema text.

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 generates 'one or many still images from text prompts,' optionally with reference images. It distinguishes itself from siblings focused on video, audio, analysis, and status by specifying image generation, and lists concrete use cases like graphics, mockups, and logos.

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 guidance: load the `generating-images` skill first, use `list_image_models` for model selection, use `job_status` for pending jobs, and never re-submit pending or failed entries. It also clarifies batching behavior and when to use dry_run, giving clear when-to vs. when-not-to context.

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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