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Duplicate a clip

duplicate_clip

Clone one clip — within the same project, or from another project — into a target project.

  • Same project: pass target_project_id and source_clip_id (omit source_project_id).

  • Cross-project: pass target_project_id, source_project_id, source_clip_id. The source clip's S3 assets (voiceover audio, original video footage, generated video, etc.) are re-hosted into the target guide's S3 namespace, so the new clip is independent of the source — deleting the source project later won't break it.

Insertion: pass after_clip_id to place immediately after a specific clip in the target. Omit to append at end.

Returns the new clip_id and its final index. Concurrency: whole-project mutation (conflict domain: the entire target project) — serialize; do not run in parallel with any other mutation on the same target_project_id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
after_clip_idNoInsert after this clip ID in the target. Appends at end if omitted.
source_clip_idYesID of the clip to duplicate.
source_project_idNoCross-project mode: ID of the project the source clip lives in. Omit for same-project duplication.
target_project_idYesProject (guide) ID to insert the duplicate into.

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: -[
-  "target_project_id",
-  "source_clip_id",
-  "context",
-  "llm_model"
-]New value: +[
+  "target_project_id",
+  "source_clip_id"
+]
  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: -[
-  "target_project_id",
-  "source_clip_id"
-]New value: +[
+  "target_project_id",
+  "source_clip_id",
+  "context",
+  "llm_model"
+]
  3. Changed2 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
  4. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint=false, destructiveHint=false), the description discloses meaningful behavioral traits: S3 assets are re-hosted into the target namespace, the duplicate is independent of the source, deleting the source project later won't break it, the conflict domain is the entire target project, and it returns the new clip_id and final index. No contradiction with 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 well-structured with bullet points, front-loads the core purpose, and every sentence serves a clear function (modes, insertion, output, concurrency). It is informative without being bloated.

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 mutation tool with cross-project side effects and concurrency constraints, the description covers all necessary operational aspects: mode selection, asset re-hosting, insertion position, return value, and serialization requirement. An agent has enough context to invoke the tool correctly without an output schema.

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% and each parameter is already described in the schema. The description restates the same-project vs cross-project combinations and after_clip_id behavior, which adds some clarity about parameter relationships but does not introduce meaning beyond the schema. Baseline 3 is appropriate.

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 ('Clone'), a specific resource ('one clip'), and the destination ('target project'), and distinguishes same-project from cross-project duplication. This clearly separates it from siblings like duplicate_project (whole project) and add_clips (adding new clips).

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 gives explicit parameter combinations for same-project vs cross-project use and explains insertion behavior with after_clip_id. It also includes a strong concurrency warning. However, it does not explicitly name alternative tools or state when not to use this tool, so it misses the highest bar for exclusion 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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