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Transcribe source media, edit Final Cut Pro timelines by spoken words, and remove filler words—no need to match timecodes manually.

Instructions

Transcribe source media locally and edit the timeline by what was SAID rather than by timecode. Also removes filler words. Actions: transcribe_media, edit_by_transcript, remove_filler_words, transcript_pack.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
argsNoArguments for the chosen action, e.g. {"filepath": "/path/to/project.fcpxml"}.
actionYesWhich operation to run.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Addedv0.22.1

TDQS

B3.1/5.0
Behavior2/5

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

No annotations exist, so the description carries the full burden; it does reveal local processing and the 'by what was SAID' approach. But it never states whether edit_by_transcript or remove_filler_words mutates the timeline/project, whether changes are reversible, or what transcript_pack returns. Important behavioral gaps remain for a tool that implies editing side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short sentences plus an action list; the description front-loads the core value proposition and is easy to scan. The filler-word sentence slightly duplicates the remove_filler_words action name but still provides immediate comprehension.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

This is a multi-action tool with no annotations and no output schema, and the description defines each action only by name, not by input/output or side-effect behavior. An agent would struggle to know which action to select in a given situation or what result to expect.

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?

The schema covers both parameters fully, including enum values and an args example, so the baseline is 3. The description repeats the action names but adds little meaning about expected args, return effects, or how each action customizes the args object.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a concrete capability—transcribe media locally and edit the timeline by spoken content rather than timecode—and lists the accepted actions, making the tool's purpose and scope clear. It distances itself from generic edit and generate siblings by emphasizing transcript-based editing and filler-word removal. However, it doesn't explicitly contrast itself with the sibling 'edit' tool.

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

Usage Guidelines3/5

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

Use cases are implied: editing by transcript or removing filler words. But there is no explicit when-to-use/when-not-to-use guidance, no named alternatives, and no exclusions. This is minimum viable but leaves routing to inference.

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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