Voxloom MCP lets an AI assistant transcribe videos and podcasts, then read, search, summarize, and manage the resulting transcripts.
Estimate transcription cost and duration before spending anything.
Transcribe a media URL (with optional speaker labels, cleanup, summary, chapters, translation, vocabulary, and spend confirmation).
Get transcript by job ID, with optional verbatim text, timestamps, and a character budget.
Search transcript for spoken phrases with timestamps and speaker matches.
Get summary with abstract, key points, quotes, and chapters.
List transcripts to find past job IDs by title or status.
Get speakers to see who spoke and for how long.
Rename speaker across the whole transcript and exports.
Cancel transcription and get a refund for unprocessed audio.
List languages to check transcribable and translatable targets.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Voxloom MCP Server transcribe https://youtu.be/VIDEO_ID and find what they say about consensus"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Model Context Protocol server for Voxloom. Lets an assistant transcribe a video or podcast, then read, search and summarise the result.
Works with Claude Desktop, Claude Code, Cursor, Cline, and anything else that speaks MCP over stdio.
You need a Voxloom API key from API keys in your dashboard. API access is included from the Studio plan upwards.
claude_desktop_config.json:
{
"mcpServers": {
"voxloom": {
"command": "npx",
"args": ["-y", "@voxloom/mcp"],
"env": { "VOXLOOM_API_KEY": "sk_prod_..." }
}
}
}claude mcp add voxloom --env VOXLOOM_API_KEY=sk_prod_... -- npx -y @voxloom/mcpBoth read the same shape of config as Claude Desktop. Add the voxloom entry
above to the MCP settings.
Related MCP server: @scriptivox/mcp-server
Tool | Charges | What it does |
| No | What a recording would cost, and whether the balance covers it |
| Yes | Transcribes a recording and returns the transcript |
| No | Reads a transcript by job id, or reports progress |
| No | Finds where something was said, with timestamps |
| No | Abstract, key points, quotes and chapters |
| No | Recent transcripts and their job ids |
| No | Who is in a transcript and for how long |
| No | Renames a speaker everywhere, exports included |
| No | Stops a running job; refunds the remainder |
| No | The 101 languages, and which can be translated into |
Transcription costs money. transcribe prices the job before submitting it
and refuses anything above a ceiling (30 minutes of audio by default) unless it
is passed confirm_spend: true. So an assistant cannot quietly spend your
balance on a three-hour video: it has to tell you the cost and ask.
Raise or lower the ceiling with VOXLOOM_MAX_CREDITS_PER_CALL. A credit is
one second of audio, so 60 credits is a minute.
Transcripts are large. Nothing returns a whole one by default. Reads are
budgeted and say plainly when they truncated, so the assistant knows it is
looking at an excerpt. For a question about one moment, search_transcript is
both cheaper and more accurate than reading everything.
Variable | Default | What it does |
| required | Your API key |
|
| Override for self-hosting |
|
| Spend ceiling per |
|
| How long |
|
| Text budget for a transcript read |
You: Transcribe https://youtu.be/VIDEO_ID and tell me what they say about consensus.
The assistant estimates the cost, transcribes it, then searches the transcript for "consensus" rather than reading all 9,000 words, and answers with timestamps you can check.
npm install
npm run typecheck
npm test # builds, then runs the tests against dist/Tests run against the compiled output rather than the sources, because that is what ships and it means the NodeNext module specifiers resolve as they will at runtime.
MIT.
Stops a running job. The unprocessed remainder is refunded; the part already transcribed stays charged, because that work was done.
Inputs: job_id (required).
What a recording would cost and how long it would take, without transcribing it or charging anything. Call this first whenever the recording might be long, or the user has not already agreed to spend. Returns the media length, the price in minutes, whether the balance covers it, and an ETA that accounts for the queue.
Inputs: url (required), model, features.
Who is in a transcript, how long each one speaks, and their speaker ids for renaming. Free. Speaking time is usually the quickest way to tell an interviewer from an interviewee.
Inputs: job_id (required).
The abstract, key points, verbatim pull quotes with timestamps, and timestamped chapters for a finished transcript. Free.
The cheapest way to find out what a long recording is about. Only present if the job was run with the summary feature.
Inputs: job_id (required).
Fetches a transcript by job id, or reports progress if it is still running. Free, and does not charge anything.
Returns a budgeted excerpt rather than the whole transcript, and says so when it truncates. For a question about a specific moment, use search_transcript instead; for the gist, get_summary.
Inputs: job_id (required), verbatim, max_chars, timestamps.
Every language that can be transcribed, and which of them can be translated into. Free. Check a target here before asking for a translation: more languages can be transcribed than translated into.
Inputs: no named parameters.
The account's transcripts, newest first, with their job ids. Free. Use this to find the id of something transcribed earlier.
Inputs: limit, search, status.
Replaces a speaker's label across the whole transcript in one operation, including in every export. Free. Get the speaker ids from get_speakers first.
Inputs: label (required), job_id (required), speaker_id (required).
Searches a finished transcript and returns matching turns with their timestamps and speakers. Free.
Prefer this over reading a whole transcript when the question is about a specific topic or moment. Note it is a literal text search, not a semantic one, so search for words that were actually spoken rather than a paraphrase.
Inputs: query (required), job_id (required), max_results.
Transcribes a recording and returns the transcript. This charges the user's account, by the length of the recording.
Waits for the result and returns it if the job finishes in time. For a long recording it returns a job id instead; call get_transcript with it shortly afterwards.
Refuses anything costing more than 30m of minutes unless confirm_spend is true. When it refuses, tell the user the cost and ask before retrying with confirm_spend.
Inputs: url (required), model, features, language, +4 more.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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Scored across 10 tools
Each tool has a clearly distinct role: estimate (free pricing/preview), transcribe (charges), get_transcript (fetch by id), search_transcript (literal search), get_summary (abstract), plus account/list/speaker lifecycle tools. Descriptions even cross-reference each other to steer selection (e.g., search vs get vs summary). No meaningful overlap.
Most tools follow a verb_noun pattern (get_transcript, search_transcript, get_summary, list_transcripts, get_speakers, rename_speaker, cancel_transcription, list_languages). The two bare verbs 'estimate' and 'transcribe' deviate slightly, but the style remains readable and predictable.
Ten tools for a transcription service is well-scoped: pricing, execution, retrieval in several modes, speaker management, cancellation, and language listing each earn their place without redundancy.
Full lifecycle is covered: estimate → transcribe → retrieve/summarize/search → manage speakers → cancel, plus language discovery. Minor gap: list_languages references translations ('check a target before asking for a translation'), implying a translate operation that no tool exposes.
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Transcribe audio, video and YouTube links, then search, summarize and export your transcripts.
Transcribe audio and video into timestamped transcripts, and search and read your transcripts.
Transcribe audio and video and work with your transcript library from any AI app. 119 languages.
