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Clueso

by io.clueso

Split clip

split_clip

Split a video clip into two clips at the given timestamp. The left clip keeps the original clip ID; the right clip gets a new ID and is inserted immediately after. Elements spanning the split point are duplicated into both clips with adjusted timing. Voiceover transcript and transcribed words are split proportionally.

Concurrency: whole-project mutation (conflict domain: the entire project) — serialize. Do not run it in parallel with ANY other mutation on the same project_id, including element/voiceover edits; run them one at a time. (Mutations to different projects run in parallel freely.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
clip_indexYesIndex of the clip to split
project_idYesThe project ID
split_timeYesTime in seconds within the clip where to split (clip-relative, not timeline-relative). Must be > 0.1s and < clip_duration − 0.1s; values outside this range are rejected.

TDQS

A4.7/5.0
Behavior5/5

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

The description goes well beyond the annotations (which only indicate mutation and non-destructiveness) by detailing key behaviors: ID assignment for left/right clips, element duplication across the split point, proportional voiceover splitting, and runtime validation of split_time. This fully compensates for the lack of annotation depth.

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 remarkably efficient: the first paragraph covers all core functionality, and the second paragraph provides essential concurrency constraints. No redundant sentences or filler words; every sentence earns its place.

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 (affecting clips, elements, transcripts) and the absence of an output schema, the description is complete. It covers validation, side effects, ID behavior, and concurrency. An agent receives sufficient information to execute the tool correctly without needing external context.

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?

Although schema coverage is 100%, the description adds significant value by explaining that split_time is clip-relative and by specifying the rejection range (>0.1s and < clip_duration-0.1s). It also elaborates on the ID and element effects, which the schema alone does not convey.

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 uses a specific verb-resource pair ('Split a video clip') and details the behavioral outcome (IDs, element duplication, voiceover splitting). It clearly distinguishes from sibling tools like 'duplicate_clip' and 'remove_clip' by specifying the split mechanics.

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

Usage Guidelines4/5

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

The description explicitly states the tool's purpose and provides concurrency guidance ('Do not run it in parallel with ANY other mutation'). While it doesn't name alternative tools for non-split operations, the context of splitting is unambiguous and the serialization advice is actionable.

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