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by io.clueso
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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. Exception: a question, results card, form, or any element with a clickaction stays whole in the left clip (trimmed to end at the split), so it is never asked or paused on twice. 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

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.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changed
    • removedInput schema / properties / context
      Removed value: -{
-  "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\"",
-  "type": "string"
-}
    • removedInput schema / properties / conversation_id
      Removed value: -{
-  "description": "Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it.",
-  "type": "string"
-}
    • removedInput schema / properties / llm_model
      Removed value: -{
-  "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
-  "type": "string"
-}
    • changedInput schema / required
      Previous value: -[
-  "project_id",
-  "clip_index",
-  "split_time",
-  "context",
-  "llm_model"
-]New value: +[
+  "project_id",
+  "clip_index",
+  "split_time"
+]
  2. Changed4 schema fields changed
    • addedInput schema / properties / context
      Added value: +{
+  "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\"",
+  "type": "string"
+}
    • addedInput schema / properties / conversation_id
      Added value: +{
+  "description": "Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it.",
+  "type": "string"
+}
    • addedInput schema / properties / llm_model
      Added value: +{
+  "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
+  "type": "string"
+}
    • changedInput schema / required
      Previous value: -[
-  "project_id",
-  "clip_index",
-  "split_time"
-]New value: +[
+  "project_id",
+  "clip_index",
+  "split_time",
+  "context",
+  "llm_model"
+]
  3. Changed3 schema fields changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • removedInput schema / additionalProperties
      Removed value: -false
    • addedInput schema / properties / clip_index / maximum
      Added value: +9007199254740991
  4. First observed

TDQS

A4.5/5.0
Behavior5/5

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

The description adds substantial behavioral detail beyond the annotations: the left clip keeps the original ID, the right clip gets a new ID and is inserted immediately after, spanning elements are duplicated with adjusted timing, interactive elements are treated as an exception, and voiceover/transcript data is split proportionally. It also discloses the whole-project conflict domain and serialization requirement, which is critical operational context not captured by the annotations.

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 front-loaded with the core operation and then presents side effects, exceptions, and concurrency in a logical order. Every sentence contributes useful information, with no filler or redundant restatement of the schema.

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?

For a project-mutating split operation with no output schema, the description covers the important effects an agent needs: ID handling, insertion order, element duplication and exceptions, transcript splitting, and concurrency constraints. The remaining omissions, such as full response shape or permission requirements, are minor relative to the behavioral detail provided.

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 meaning of project_id, clip_index, and split_time is already fully documented in the schema. The description confirms that splitting occurs at a timestamp but does not add parameter-specific semantics beyond what the schema already provides.

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 states a specific verb and resource: splitting a video clip into two clips at a timestamp. It also specifies the resulting left and right clips and their ID behavior, which makes the operation unmistakable and distinguishable from siblings such as duplicate_clip or remove_clip.

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 clearly establishes the operation context and gives an important concurrency restriction: serialize the mutation and do not run it in parallel with any other project mutation. It does not explicitly name alternative sibling tools like duplicate_clip or update_clips, so the tool-selection guidance is strong but not fully comparative.

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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