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by ai.switchapp

Manage Voices

voice

Your saved voices — one tool for the whole voice library. Users speak plain language and never know ids: resolve every voice by NAME yourself (call action "list" first if unsure) and never ask the user for an id. action="list" returns every saved voice with voice_id, name, kind and ready — kind "reference" is an instant voice match saved from a clip and kind "clone" is a trained voice (both speak through generate_audio: pass the NAME as its voice param); kind "avatar" voices drive talking_avatar_video. action="create" saves a NEW reference voice from a clip: voice_name plus audio_url (e.g. the url upload_media returned) or audio_base64 (+ format) — free, ready instantly. action="rename" renames a saved voice (voice_id takes the id OR the current name, new_name is the new name). action="clone" registers a voice for talking_avatar_video from audio_sample_url + voice_name (charged 2 credits). action="delete" removes a voice by voice_id or name.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYesWhich operation to run.
formatNocreate: clip format when sending audio_base64. Default wav.
new_nameNorename: the new name for the voice.
voice_idNodelete/rename: the voice id OR its name — names are resolved for you.
audio_urlNocreate: URL of a 10-30 second clip of the voice — e.g. the url returned by upload_media.
voice_nameNocreate/clone: what to call the voice (unique per account).
audio_base64Nocreate: the clip as base64 when there is no URL.
audio_sample_urlNoclone: a 10-30 second voice sample URL (reachable).

TDQS

A4.8/5.0
Behavior5/5

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

The description discloses significant behavioral details beyond the sparse annotations: actions are mutating (create, rename, clone, delete), clone costs 2 credits, create is free and ready instantly, audio clips must be 10–30 seconds, and voice names must be unique per account. It also explains the 'kind' semantics and that names are resolved to ids, making the tool's side effects and constraints transparent.

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 long but every sentence earns its place; it is front-loaded with the core value proposition and the key rule about resolving names, then structured action-by-action. The density is justified by the tool's many actions and parameters, and there is no redundant filler or repetition of schema field names.

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?

The description covers all actions, parameter usage, naming policy, relationships to other tools, and cost constraints. It explicitly states what 'list' returns (every saved voice with voice_id, name, kind, ready), but it does not mention return values for create, rename, clone, or delete, and does not describe error conditions. Despite the lack of an output schema, the description is largely complete for an agent to select and invoke the tool correctly.

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?

Although the input schema already documents every parameter (100% coverage), the description adds cross-action meaning: voice_id can be a name, audio_url can be the result of upload_media, format defaults to wav, and voice_name uniqueness across the account. It also explains the purpose of parameters in the context of each action, going beyond simple 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 states the tool manages a voice library with a clear verb and resource ('Your saved voices — one tool for the whole voice library'). It explicitly enumerates five actions (list, create, rename, clone, delete) and distinguishes voice kinds (reference, clone, avatar) and how they relate to sibling tools like generate_audio and talking_avatar_video, effectively separating it from siblings.

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

Usage Guidelines5/5

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

The description gives explicit when-to-use guidance: resolve voices by name, call list first if unsure, never ask the user for an id. It also describes each action's trigger scenario (e.g., clone for avatar voices, create from clip via audio_url or audio_base64) and notes that avatar voices drive talking_avatar_video, providing clear context for choosing the right action.

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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