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Talking Avatar Video

talking_avatar_video

Turn a face photo into a lip-synced talking-head video that speaks your text (or your audio). Provide image_url (a clear face photo) and either script (text to speak, max 2500 characters) or audio_url. Optional voice_id / language / voice_settings. Renders in ~1-5 minutes (single call, returns the finished branded video) and is saved to your library. Charged per video.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scriptNoText the avatar speaks. Max 2500 characters. Required unless audio_url is given.
languageNoOptional language code (default en).
voice_idNoOptional voice id (from clone_voice / your library).
audio_urlNoPre-recorded audio URL to lip-sync instead of generating speech from script.
image_urlYesA clear face photo (Switch/public URL). Required.
voice_settingsNoOptional: { stability, similarityBoost, style, useSpeakerBoost } 0-1.

TDQS

A4.4/5.0
Behavior5/5

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

The description adds substantial behavioral context beyond the minimal readOnlyHint: it discloses the ~1-5 minute render time, that it returns a finished branded video in a single call, that the video is saved to the library, and that it is charged per video. These details are crucial for an agent to manage cost, expectations, and workflow and are not present in the annotations.

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 concise and well-structured: it starts with the core purpose, then details required/optional parameters, and finishes with timing, invocation model, persistence, and cost. Each sentence contributes unique information without redundancy, making it easy to parse quickly.

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 tool with 6 parameters, a nested object, and no output schema, the description covers the key aspects: inputs, optional parameters, behavior (render time, single-call), and consequences (saved, charged). However, it does not describe the exact output format (e.g., video URL or ID) or specify failure/error behavior, leaving minor gaps for an agent to infer.

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 coverage is 100%, so the schema already documents all parameters. The description reinforces the either/or relationship between script and audio_url and notes the character limit, but it adds minimal new semantics beyond what the schema's parameter descriptions already provide. 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 clearly states the tool's function: converting a face photo into a lip-synced talking-head video that speaks text or audio. The verb 'Turn' and specific resource ('face photo' → 'talking-head video') make it unambiguous, and mentions of 'branded video' and 'saved to your library' differentiate it from similar siblings like lip_sync_video.

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 input requirements: image_url plus either script or audio_url, and lists optional parameters. It gives context on how to invoke the tool correctly, though it does not explicitly state when to choose this tool over alternatives such as lip_sync_video or generate_video, or any exclusions.

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.

Resources