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Clueso

by io.clueso

Create clueprint

create_clueprint

Create a new clueprint (reusable video template). A clueprint is a structured file directory with design notes, composition rules, and screenshots that can be used to generate videos.

Use human-readable folder categories: design/ (colors, typography, backgrounds, animation), rules/ (composition, voiceover, editing), screenshots/ (rendered clip previews). See the "Creating Clueprints" section in the system instructions for the full structure.

If source_guide_ids are provided, guide data is automatically extracted clip-by-clip into the clueprint as reference material.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesName of the clueprint
visibilityYes'private' = only you, 'workspace' = everyone in your workspace
descriptionNoDescription of what this clueprint does
source_guide_idsNoIDs of existing projects to extract template data from

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint=false and destructiveHint=false, so the description is not required to repeat that. It adds value by disclosing the automatic extraction behavior ('guide data is automatically extracted clip-by-clip') and the structured folder categories, which are behavioral traits beyond the annotations. No contradiction detected.

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?

Three sentences, all essential: definition, usage advice, and conditional behavior. No filler, front-loaded with the primary purpose. Each sentence earns its place.

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 adequately covers the purpose, usage, and a key side effect (auto-extraction). It references external documentation for full structure, which is acceptable. However, it does not mention what the tool returns (e.g., clueprint ID) nor any next steps. Given no output schema, a slight gap exists, but overall it is sufficient for an agent to invoke correctly.

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 baseline is 3. The description adds meaningful extra context: for source_guide_ids, it specifies 'clip-by-clip' extraction and 'reference material', which the schema lacks. It also orients the agent toward folder categories, though these are not directly tied to a parameter. This exceeds the schema alone.

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 'Create a new clueprint' with a specific verb and resource. It defines a clueprint as a 'reusable video template' and explains its structure (file directory with design notes, rules, screenshots). This effectively distinguishes it from sibling tools like get_clueprint or update_clueprint.

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 description provides usage hints such as using human-readable folder categories and referencing the system instructions, but it does not explicitly compare this tool to siblings like create_project. There is no guidance on when not to use it or alternatives, leaving the agent to infer from the sibling list. The conditional behavior with source_guide_ids offers some contextual guidance.

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