BlitzReels Video Editor

Generate Image

generation_image_create

Idempotent

Queue an AI image generation into the BlitzReels media library. Spends AI credits and returns a job to poll with generation_jobs_get.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`model`	No	Image model. Call generation_options_list for costs.	fal-ai/nano-banana-2
`prompt`	Yes	What the image should show (8-5000 characters).
`folderId`	No	Optional media library folder ID.
`aspectRatio`	No	Output aspect ratio.	1:1
`workspaceId`	No	Optional workspace ID. Defaults to the user's default workspace. UUID string.
`idempotencyKey`	No	Retry key. Reuse only with identical inputs.
`referenceAssetIds`	No	Up to 4 existing image asset IDs to use as style or subject references.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`generation`	Yes

TDQS

A4/5.0

Behavior4/5

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

Annotations already mark it as non-read-only and idempotent, and the description adds important behavioral details: it spends AI credits and returns an async job rather than an immediate image asset. This meaningfully supplements the annotation metadata.

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, efficiently front-loading the core action and then noting cost and polling behavior. Every sentence earns its place with no redundancy or filler.

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?

Given that an output schema exists, the description does not need to detail return values. It covers the key execution flow—queue, credits, job identifier, and polling—which is sufficient for correct invocation in most cases.

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 the schema fully documents all 7 parameters. The description adds little beyond the general effects of calling the tool, which is acceptable given the schema already carries the parameter semantics.

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 specific action ('Queue an AI image generation'), the target resource ('BlitzReels media library'), and its async nature. It distinguishes itself from sibling generation tools by mentioning image generation and the polling job flow.

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

Usage Guidelines3/5

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

The context that this queues an AI image generation is clear, and it points to generation_jobs_get for polling. However, it does not explicitly contrast with other generation siblings or state when not to use it, leaving some routing to inference.

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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TDQS

B3.1/5.0

Disambiguation3/5

Most tools use clear domain prefixes, but several boundaries are fuzzy: clips_get and clips_manage both expose clip/export status, and update_timeline_clip overlaps with timeline_edit_apply for trim/duration changes. Detailed descriptions mitigate this, but an agent could still select the wrong tool for clip inspection or timeline edits.

Naming Consistency3/5

All names are snake_case and mostly readable, but the server mixes verb-first names (add_text_overlay, delete_timeline_items, update_timeline_clip) with noun-first domain-action names (clips_create, generation_*_create, media_import_*). The domain-prefix pattern dominates, yet the inconsistent verb placement makes the convention only partially predictable.

Tool Count1/5

58 tools is far beyond the 25+ threshold and matches the rubric's 50+ extreme-mismatch example. Even though the domain is broad, this surface would be easier for an agent to navigate if split into focused servers for media, generation, timeline editing, clips, and workflows.

Completeness3/5

Core video-editing flows are well represented: project creation/inspection, timeline item editing/deletion, media import/upload, AI generation, exports, and clips. However, lifecycle gaps remain—no project update/delete, no media asset deletion, no delete for characters or story kits, and no export cancellation—so some user requests will dead-end.

Resources

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