Can I separate who spoke when with the AirCaps API?

Yes. Create a diarization from a recording and the API returns the speaker turns, which you can also download as an RTTM file. To attach names to those turns, enrol each voice as a speaker and run synchronous speaker identification, which returns the best-matching speaker for a clip in the same call.

What are the rate limits for the AirCaps API?

The OpenAPI spec does not publish numeric rate limits for the AirCaps API; the account and usage operations report your current consumption so you can track it. For current limits check the vendor documentation at https://docs.aircaps.com.

How do I transcribe an audio file with the AirCaps API through Jentic?

Import the AirCaps API from the Jentic API Directory, then have your agent search by intent such as 'transcribe an audio file' and Jentic returns the create-transcript operation with its input schema. The agent uploads the file, creates the transcript, and downloads the subtitles once it is ready. To run it on your own infrastructure, install Jentic One from its GitHub repo.

Can I limit what my agent is allowed to do with the AirCaps API?

Yes. AirCaps carries the transcript and speaker ids in the URL path, so write a rule that allows only the create and read operations your task needs, and your agent can transcribe and read results but cannot delete transcripts, speakers, or enrolments. Add the delete operations only when the agent genuinely needs them, and every call it makes is logged.

Is there an AirCaps MCP server?

You don't need an MCP server to give your agent the AirCaps API. Jentic connects it directly from the API Directory: import it, store your API key once, and your agent calls the transcript, diarization, and speaker operations on demand without loading another server's tool definitions into its context.

Does the AirCaps API support languages other than English?

Per the spec the transcription models are described for English, and the models listing reports the engines available for each task so you can confirm what a given model supports. Diarization and speaker identification work from the audio signal rather than the words, so they are not tied to a single language.