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Glama

Morpha

by ai.morphareels

transcribe_clip

Return the cached transcript of a clip's audio. Generated client-side in the Morpha editor (transformers.js Whisper) and cached in R2 next to the clip; if absent, it is produced when the clip is opened in the editor. Independent of the clip's video codec — runs on the audio track only, so it works on HEVC/AV1 clips that the OCR pipeline can't decode. Returns { ok: true, status: 'ready' | 'not-ready', data: { text, word_count, words: [{ word, start, end }], vtt? } }.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
clipYesClip filename (video.<id>.clip).
projectIdYesProject the clip belongs to.

Schema Changelog

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

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden and does so thoroughly. It discloses the client-side Whisper generation path, R2 caching location, lazy generation on editor open, audio-only processing, codec independence, and the exact status values including 'not-ready'.

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 three sentences with no wasted words. The first sentence states the core function, the second explains the generation and caching behavior, and the third provides codec context and the return contract. Every sentence earns its place.

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?

Despite having no output schema, the description includes a complete return shape with status values, data fields, word objects, and optional VTT. It also covers runtime behavior, caching, and compatibility, leaving little uncertainty about how to invoke and interpret the tool.

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%, with both parameters (projectId and clip) already documented. The description adds no additional parameter-specific semantics, but it does reinforce that the tool works on a clip within a project. 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 names a specific verb and resource: 'Return the cached transcript of a clip's audio.' It further distinguishes itself by highlighting that it operates on the audio track independently of video codec, which separates it from OCR-based tools like describe_video or detect_text_regions.

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 provides clear context for when to use the tool: when a cached audio transcript is needed, including for HEVC/AV1 clips that the OCR pipeline cannot decode. It does not explicitly name alternatives or exclusions, but the codec independence and cache semantics make the intended use case apparent.

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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TDQS

A3.6/5.0
Disambiguation3/5

The tool set has several clusters that overlap conceptually: add_keyframe/add_keyframes/set_keyframes_batch, move_band/move_layer/shift_group/set_group_window, and fade_layer/set_layer_transition/set_layer_visible all target similar actions. However, each tool's description is distinct and detailed enough for an agent to disambiguate after careful reading. The sheer volume of 95 tools still creates meaningful selection risk.

Naming Consistency4/5

Tool names overwhelmingly follow a verb_noun snake_case pattern (add_, remove_, set_, list_, rename_, create_, delete_), which is predictable and readable. Minor inconsistencies exist: plural variants like add_keyframes vs add_keyframe, mixed specificity like remove_keyframe vs remove_color_keyframe, and a few oddballs like reid_project and clip_processing_status. Overall the pattern is strong and helps navigation.

Tool Count2/5

95 tools is far beyond the well-scoped range and will overwhelm agents; even a comprehensive video editor could consolidate keyframe batch operations, overlay/track manipulations, and page/group window controls. Many tools are highly specific (reid_project, safe_zones, move_band) and place burden on the agent to pick among near-synonym verbs. While the broad domain justifies many operations, this count is excessive for a coherent tool surface.

Completeness3/5

The surface covers a broad editing lifecycle: layers, keyframes, text, captions, audio, pages, groups, versions, projects, workspaces, and collections, including batch operations. However, there is no MCP tool to render/export the final MP4 (explicitly delegated to an SDK) and no upload_video tool for bringing in a clip file, leaving major end-to-end gaps. For the stated purpose of building and editing videos, these are significant omissions that force agents to stop short of delivery.

Resources