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Transcribe audio

carpe_transcribe

Transcribes speech from audio URLs, data URIs, or local files (mp3, wav, m4a, flac, ogg, webm) into text using Whisper-compatible ASR with language and vocabulary hints.

Instructions

Speech to text (Whisper-compatible). audio: https:// URL, data: URI, or local path (stdio). Formats such as mp3, wav, m4a, flac, ogg, webm.

Input Schema

NameRequiredDescriptionDefault
audioYes
modelNoASR model (default openai/whisper-large-v3). See carpe_list_models type "asr".
routeNo"carpe" → /v1/audio/* (Carpe TEE network). "router" → /router/audio/* (cheapest market). Default from CARPE_DEFAULT_ROUTE.
promptNoContext / vocabulary hint.
languageNoISO-639-1 hint, e.g. "fr".
response_formatNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

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

Annotations cover the safety profile (destructiveHint=false, openWorldHint=true), so the bar is lower. The description adds real context the annotations don't: the audio argument can be a remote URL, a data URI, or a local path over stdio, and it lists accepted formats. It says nothing about cost, latency, or job/response behavior, and readOnlyHint=false is never explained.

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?

Two short sentences, zero filler, with the core purpose front-loaded before the input-form details. Every clause carries information an agent would otherwise have to guess.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description should say what comes back, especially since `response_format` (json/text/verbose_json) is an enum with no explanation anywhere. Input side is well covered; the return-value side is a real hole for a 6-parameter 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?

Schema coverage is 67%, and the description compensates for the undocumented required `audio` parameter by spelling out its accepted forms (URL, data URI, local path) and container formats — much more than the bare `type: string` in the schema. It adds nothing for model, route, prompt, language, or response_format, which the schema already documents.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: "Speech to text (Whisper-compatible)", immediately distinguishable from the sibling carpe_tts which goes the other direction. It does not explicitly name that sibling, but the direction of the operation is unambiguous.

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

Usage Guidelines2/5

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

There is no when-to-use or when-not guidance, no mention of when to pick this over carpe_tts / carpe_chat, and no note about cost or credit implications. Usage is only implied by the one-line purpose statement.

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