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Clueso

by io.clueso

Generate AI media (image or animation)

generate_media

Generate an AI image or canvas-code-based animation directly into a clip.

  • kind="image": text-to-image. Pass prompt. Optional: style_id (from find type='image_gen_style_packs'), reference_image_url or mcp_upload_id for image-to-image grounding.

  • kind="animation": canvas-code animation rendered from a prompt. Pass prompt. Optional: voiceover_text (drives timing), base_component_id (reuse a saved animation as the starting point), reference_image_url or mcp_upload_id for visual grounding.

Generation is asynchronous: the element is created immediately with a stable element_id and rendered in the background. Poll get_clip (the phantom flag drops once rendering completes).

Tip: use this tool whenever the user asks for a "generated", "AI", or "create me a" visual. For uploaded photos / logos / icons / GIFs, use add_elements with element_type='image' and a src or mcp_upload_id instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
xYesTop-left X in canvas pixels.
yYesTop-left Y in canvas pixels.
kindYes'image' = AI text-to-image; 'animation' = canvas-code-based motion graphic.
widthYesWidth in pixels.
heightYesHeight in pixels.
promptYesGeneration prompt. For animations, be SPECIFIC: name the UI elements, interaction sequence, timing feel, and visual style. Vague prompts produce bad output.
clip_idYesClip ID to place the generated element into.
end_timeNoDisappear at (seconds).
style_idNoImage only. Style preset ID from find(type='image_gen_style_packs'). See resource clueso://docs/generation-styles.
project_idYesProject ID.
start_timeNoAppear at (seconds).
mcp_upload_idNomcp_upload_id from the upload flow. Resolved server-side to a presigned URL before generation.
voiceover_textNoAnimation only. Paces the motion to the spoken script — and as a side effect sets this clip's voiceover text and triggers speech generation for the clip.
base_component_idNoAnimation only. Reuse a saved animation component as the starting point (from find(type='element_components')). To re-skin its tunable parameters, set parameter_values via update_elements after it renders.
reference_image_urlNoPublic URL of a reference image. Mutually exclusive with mcp_upload_id.

TDQS

A4.8/5.0
Behavior5/5

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

The description reveals that generation is asynchronous, with immediate element_id creation and background rendering, and instructs to poll get_clip for completion. It also discloses side effects of voiceover_text (sets voiceover and triggers speech generation). These details go well beyond the sparse annotations (readOnlyHint=false, etc.) and provide essential behavioral context.

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 concise and well-structured: a one-line summary, bulleted breakdown by kind, async behavior note, and a usage tip. Every sentence is necessary and adds value, with no redundancy or fluff.

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?

The description covers the two generation modes, async behavior with polling instructions, and alternatives. It is thorough for a tool with 15 parameters and complex behavior. However, it omits details on error handling, timeouts, or what happens on generation failure, which would enhance completeness.

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 description coverage is 100%, so each parameter is already documented. However, the tool description adds significant extra meaning: grouping parameters by kind, explaining optional parameters' sources (e.g., style_id from find), mutual exclusivity of reference_image_url and mcp_upload_id, and the side effect of voiceover_text. This raises the value above baseline 3.

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 AI images or canvas-code animations into a clip. It distinguishes between two kinds (image and animation) and explicitly contrasts with the sibling tool add_elements for uploaded media, making the boundary unambiguous.

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?

The description provides explicit guidance: 'use this tool whenever the user asks for a generated, AI, or create me a visual' and 'for uploaded photos / logos / icons / GIFs, use add_elements'. It also explains when to use each kind and optional parameters, giving clear decision criteria.

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

A4.1/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but there is notable overlap between remove_elements and remove_from_project(target='element'), which both remove elements from a clip. This duplication could cause an agent to misselect. Otherwise, tools like add_clips, add_elements, add_audio, and analyze_audio are well-differentiated.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern (e.g., add_clips, create_project, get_clip, update_elements). There are no camelCase or mixed conventions. Even compound names like voiceover_batch and auto_sync fit the pattern. This makes the tool set predictable for an agent.

Tool Count2/5

With 40 tools, the set is significantly larger than the 3-15 range that typically earns its place. While the domain of video creation is broad, several tools seem redundant (remove_elements vs remove_from_project) or narrowly scoped (get_design_guide, get_element_schema), inflating the count. The number feels heavy for the apparent scope.

Completeness4/5

The tool surface covers most lifecycle operations: create, read, update, delete for projects, clips, elements, audio, articles, and clueprints. Minor gaps exist, such as no explicit tool to delete a voiceover (only mute via update_clips) and no folder management beyond listing. Overall, agents can accomplish full workflows with few workarounds.

Resources