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Remove spoken words

remove_words
Destructive

Text-based editing: cuts the given transcript words OUT of a clip as ripple deletes (everything after each cut shifts left). wordIndices are positions in get_transcript's words array for the clip's media. Contiguous indices merge into single cuts. The go-to tool for removing filler words or tightening an interview.

Input Schema

NameRequiredDescriptionDefault
tokenYesThe session token `connect` returned. Pass it on every call — it says which browser to drive.
clipIdYesThe clip whose speech is being edited.
projectIdYesProject id from get_projects.
timelineIdYesTimeline containing the clip.
wordIndicesYesIndices into get_transcript words, e.g. [4,5,6,12].

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare destructiveHint=true and idempotentHint=false, but the description adds the crucial consequence beyond them: each cut is a ripple delete where 'everything after each cut shifts left,' so downstream timing changes. It also notes contiguous indices merge into single cuts, which explains the resulting cut structure. Missing only reversibility/undo guidance for a destructive mutation.

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?

Four tight sentences, front-loaded with the operation and its effect, then the index semantics, then the use case. No filler or restatement of the name.

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?

For a destructive, non-idempotent mutation with no output schema, the description covers what changes and how subsequent content reflows. It stops short of stating what is returned or how to recover from a bad edit, which would matter for a destructive tool.

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 coverage is 100%, so baseline is 3; the description earns above that by tying wordIndices to a specific source ('positions in get_transcript's words array for the clip's media') and documenting merge behavior for contiguous values, which the schema does not say.

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?

States a specific verb and resource ('cuts the given transcript words OUT of a clip') plus the mechanism (ripple deletes). This clearly separates it from remove_clips, remove_silence, and ripple_delete_ranges, which operate on different inputs (whole clips, audio silence, time ranges).

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 go-to tool for removing filler words or tightening an interview' gives a concrete use case, and the reference to get_transcript establishes the required prior step for obtaining wordIndices. It implies but never explicitly states when to prefer remove_silence or ripple_delete_ranges instead.

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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