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Glama

Clone a voice

carpe_voice_clone

Create a temporary text-to-speech voice handle for carpe_tts by uploading a short one-speaker audio sample and confirming consent; handles expire in about 7 days.

Instructions

Upload a short reference sample (one speaker) and get a vv_… voice handle for carpe_tts (route "carpe", same model). The upload itself is free but needs a funded account. ONLY with consent: the voice must be the user's own, or its speaker must have agreed to be cloned. The handle expires (~7 days).

Input Schema

NameRequiredDescriptionDefault
modelNoTTS model that clones (default tts-chatterbox-hd).
sampleYesAudio sample: https:// URL, data: URI, or local path (stdio).
consentYesMust be true: the voice is the user's, or its speaker consented to cloning.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

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

Adds substantial facts the annotations do not carry: the upload itself is free but requires a funded account, the returned handle expires in roughly 7 days, and cloning is consent-gated. One nuance: the sample may be an https:// URL, which implies an outbound fetch despite openWorldHint=false — a mild tension an agent should not have to resolve on its own. No error or failure-mode behavior is described.

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?

Three front-loaded sentences with no filler; the action and payoff come first, constraints second. The parenthetical '(route "carpe", same model)' is jargon-dense and slightly slows comprehension, but nothing is expendable.

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?

With no output schema, the description still conveys the return value (a vv_… handle) and its lifetime, plus the cost and consent preconditions — enough to call it correctly and to know what comes back. Remaining gaps are minor: failure behavior for rejected samples or unfunded accounts.

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 all three parameters are already documented, and the baseline is 3. The description adds modest meaning — the sample should be short and single-speaker, and the clone uses the same model as carpe_tts — but says nothing about the model parameter's tradeoffs beyond the schema's default.

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?

Names a concrete verb and resource (upload a reference sample, clone a voice) and states the outcome explicitly: a vv_… voice handle for carpe_tts. It routes the agent to the consuming sibling (carpe_tts, route "carpe", same model), so it is distinguishable from every other sibling without opening a schema.

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?

Gives real preconditions: a short sample with exactly one speaker, a funded account, and consent that must be genuine. It also states the handle's downstream use via carpe_tts. It stops short of explicit negative guidance (e.g., when to reuse an existing handle instead of re-cloning, or what to do if the sample has multiple speakers).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.