switch

Lip Sync Video

lip_sync_video

Lip-sync audio onto one of your videos. RECOMMENDED: action="create" with engine="best" + video_url + sound_file (base64 data URI) — syncs the whole clip on the highest-quality engine, no face step needed. Kling flow (manual timing control): (1) action="identify-face" with video_url (MP4/MOV, 2-60s, <=100MB, 720p/1080p); (2) action="create" with session_id + face_id + audio + timing IN MILLISECONDS (sound_start_time, sound_end_time, sound_insert_time) + optional speech_volume/original_audio_volume (0-100); (3) action="status" with the task_id to poll — returns a branded SwitchApp view_url when done. Charges credits on create; failed jobs are refunded.

Input Schema

TableJSON Schema

Name	Required	Description
`action`	Yes	Which step to run.
`engine`	No	create: "best" = highest-quality whole-clip sync (needs only video_url + sound_file). Default "kling" (timeline flow).
`face_id`	No	create: a face_id from identify-face (one face supported).
`task_id`	No	status: the task_id from create.
`audio_id`	No	create: alternative to sound_file — an existing audio id.
`video_url`	No	identify-face: the source video (MP4/MOV, 2-60s, <=100MB, 720p/1080p). Use a SwitchApp/public URL.
`session_id`	No	create: from identify-face.
`sound_file`	No	create: base64 data URI of the audio (e.g. data:audio/mpeg;base64,...).
`speech_volume`	No	create: how loud the new speech is, as a percent 0-100 (default 100).
`sound_end_time`	No	create: audio end, in MILLISECONDS.
`sound_start_time`	No	create: audio start, in MILLISECONDS.
`sound_insert_time`	No	create: where in the video to place the audio, in MILLISECONDS.
`original_audio_volume`	No	create: how loud the clip's own sound stays, as a percent 0-100 (default 0).

TDQS

A4.7/5.0

Behavior5/5

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

Beyond the readOnlyHint=false annotation, the description discloses meaningful behavioral traits: it charges credits on create, refunds failed jobs, and returns a branded SwitchApp view_url on status. It also clarifies that the 'best' engine needs no face step, which is valuable operational context.

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 dense but well-structured: it front-loads the recommended path, then presents the alternative flow in numbered steps. Every sentence earns its place—no filler or redundancy exists despite the tool's complexity.

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 complex 13-parameter tool with no output schema, the description is remarkably complete. It covers both workflows, parameter dependencies, timing units, volume controls, credit charges, and the status polling return. An agent has enough context to successfully invoke any action of this 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?

The input schema covers 100% of parameters, so the baseline is 3. The description adds value by grouping parameters into workflows (e.g., best path needs video_url + sound_file; Kling path needs session_id + face_id + timing), clarifying the base64 data URI format, and noting timing is in milliseconds. This assembly guidance goes beyond the schema's isolated 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 precise verb+resource: 'Lip-sync audio onto one of your videos.' It clearly distinguishes the tool from siblings by focusing on syncing audio to an existing video and lays out the recommended engine path versus the Kling flow, making the purpose immediately evident.

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 explicit usage steps: a RECOMMENDED action/engine combination and a detailed Kling workflow with numbered actions (identify-face, create, status). It explains when to use each action and which parameters belong to each flow. However, it does not directly compare this tool to sibling alternatives like talking_avatar_video, so it falls just short of a 5.

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

A3.5/5.0

Disambiguation2/5

Several tools occupy nearly identical semantic ground: apply_iphone_realism and apply_ugc both describe casual phone-shot looks, upload_media and upload_reference_asset both accept uploads, and analyze_video overlaps heavily with analyze_video_report. The many apply_* style tools are essentially one tool parameterized by style, so agents can easily select the wrong one.

Naming Consistency4/5

Most tools follow a clear verb_noun snake_case pattern such as generate_image, list_my_videos, get_editor_run, and upscale_video. A few outliers like voice, talking_avatar_video, and video_to_prompt do not use the same verb-first convention, but they are still readable and do not create significant confusion.

Tool Count1/5

At 55 tools, the surface is far beyond what is appropriate for an MCP server; many of these be collapsed or parameterized, especially the 10 apply_* style wrappers and several overlapping upload/status helpers. Even for a broad media platform, this scale forces a huge context window and makes selecting the right tool impractical.

Completeness2/5

The surface covers generation, media display, video analysis, and Editor workflows well, but there are obvious gaps in library lifecycle management: move_asset and create_folder are referenced in tool descriptions without being exposed, and there is no clean way to delete or reorganize media assets. Agents following the descriptions will try to call tools that do not exist.

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