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by io.clueso
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Add a sync point at a word + timestamp

add_sync_point

Insert one sync marker on a clip's transcript.

Use this when:

  • The user is explicit about WHERE the camera should pause / cut (e.g. "sync the word 'submit' to 4.2s of the demo").

  • auto_sync ran but missed a step you care about.

How matching works:

  • word: case-insensitive, punctuation-stripped. The first match in the transcript is used unless occurrence > 1.

  • occurrence: 1-indexed — pass 2 to target the SECOND time that word appears, 3 for the third, etc. Required when the word repeats.

  • timestamp_seconds: clip-relative seconds. When the clip has run TTS already (generated_timestamps present), the server inverse-maps this to original-recording seconds automatically.

Constraints: the clip MUST be a video clip with a source recording (otherwise the frame thumbnail can't be extracted). The transcript must already contain the word — if not, you'll get word_not_found with a 200-char excerpt of the transcript to help you retry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
wordYesTarget word (case-insensitive; trailing punctuation is stripped).
clip_idNoClip ID (preferred). If omitted, clip_index is used.
clip_indexNoZero-based clip index. Ignored if clip_id is provided.
occurrenceNo1-indexed match number when the word repeats. Defaults to 1 (first occurrence).
project_idYesProject ID.
sync_point_nameNoOptional label (e.g. 'Click submit'). Defaults to 'Sync point N' using the next order number.
timestamp_secondsYesClip-relative timestamp in seconds. Auto-mapped to original-recording seconds when the clip has generated_timestamps.

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",
-  "word",
-  "timestamp_seconds",
-  "context",
-  "llm_model"
-]New value: +[
+  "project_id",
+  "word",
+  "timestamp_seconds"
+]
  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",
-  "word",
-  "timestamp_seconds"
-]New value: +[
+  "project_id",
+  "word",
+  "timestamp_seconds",
+  "context",
+  "llm_model"
+]
  3. Changed4 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
    • addedInput schema / properties / occurrence / maximum
      Added value: +9007199254740991
  4. First observed

TDQS

A5/5.0
Behavior5/5

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

Beyond the minimal annotations, the description discloses matching behavior (case-insensitive, punctuation-stripped, first match by default), occurrence indexing semantics, timestamp mapping when generated_timestamps exist, and the word_not_found error with an excerpt. This gives the agent a strong mental model of tool behavior.

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 organized into clear sections: use cases, matching behavior, and constraints. Every sentence carries useful information, and the most important purpose and usage guidance appear first. Despite its length, nothing feels redundant.

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 tool with 7 parameters, no output schema, and minimal annotations, the description covers prerequisites, matching rules, timestamp handling, and error recovery. The only minor omission is the success return shape, but that is not necessary to invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although schema coverage is 100%, the description adds meaningful semantics: occurrence is 1-indexed and required for repeated words, timestamp_seconds is clip-relative and auto-inverse-mapped, and clip_id vs clip_index selection behavior is clarified. This goes well beyond the schema's field descriptions.

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 opens with a specific verb and resource: 'Insert one sync marker on a clip's transcript.' This clearly distinguishes it from the sibling auto_sync, which performs automatic sync, and from other clip-editing tools like split_clip.

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?

It gives explicit when-to-use conditions: the user is explicit about where to pause/cut, or auto_sync missed a step. It also states the key constraint that the clip must be a video clip with a source recording, and warns when the word is not found, which effectively tells the agent when not to proceed.

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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