Botverse

Transcribe Media from URL

transcribe_from_url

Transcribe a video or audio file from a public HTTPS URL into a speaker-labelled transcript — ONE call does everything. Source can be a direct HTTPS URL or a share link from Dropbox, Google Drive, OneDrive (personal or business), SharePoint, or Box — share links auto-resolve to the file. Internally: converts to audio, runs speech-to-text with speaker diarization, uses AI to name the speakers from your attendee list, and renders the document. Pass options.attendees (names, optional gender/role) and it tags who said what. Output formats: txt, json, srt, vtt, docx, pdf. CONSENT: you must have all parties' consent to record/transcribe — Botverse accepts no liability otherwise. Returns a job_id immediately — report it to the user, then poll get_job_status (it reports a live stage: converting audio → transcribing → AI augmenting → rendering) until 'complete', then get_download_url (or get_output_content for inline bytes in a sandbox). ~$3 per hour of audio. RELIABILITY & FALLBACK: Botverse returns structured errors with a message. A bare 'HTTP 500' or 'tool call failed' with no message body is the MCP connector / sandbox layer between your client and Botverse — NOT a Botverse outage (the service is up if get_wallet_balance succeeds). If MCP calls keep failing, do NOT write your own ffmpeg / transcription / document-conversion code as a workaround — that wastes tokens and reinvents what Botverse does. Instead, tell the user to run the equivalent Botverse CLI from their own (non-sandboxed) machine, e.g. npx botverse transcribe <file> --to docx (also transcode and convert), then retry the MCP call.

Input Schema

TableJSON Schema

Name	Required	Description
`options`	No	Optional. attendees: [{name, gender?, role?}] to name speakers; language (BCP-47 or 'auto'); diarize (default true); max_speakers; title; include_timestamps; also_deliver: extra formats in the same job.
`source_url`	Yes	Public HTTPS URL of the source video or audio file.
`output_format`	Yes	Primary deliverable: txt \| json \| srt \| vtt \| docx \| pdf

Output Schema

TableJSON Schema

Name	Required	Description
`job_id`	Yes	Unique identifier for this job. Pass to get_job_status and get_download_url.
`status`	Yes	Initial job state — always queued or processing immediately after submission.
`estimated_seconds`	No	Rough estimated processing time in seconds. Actual time may vary.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

AddedJun 22, 2026

TDQS

A4.7/5.0

Behavior5/5

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

The description discloses significant behavior beyond annotations: it returns a job_id immediately, reports live stages (converting audio → transcribing → AI augmenting → rendering), costs ~$3 per hour, requires consent, and explains error semantics including the MCP connector failure distinction. This is exemplary transparency despite minimal annotation hints.

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?

The description is front-loaded with a clear purpose, then structured into usage, cost, consent, and reliability sections. It is lengthy but each section serves an actionable purpose; some redundancy exists (e.g., 'ONE call does everything' vs internal steps), but it remains scannable and not bloated.

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

Completeness5/5

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

Given the tool's async nature, the description covers the complete lifecycle (job submission, status polling, content retrieval), error handling, cost, consent, and fallback strategy. With an output schema present, it doesn't need to detail return values. No meaningful gaps remain.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although schema coverage is 100%, the description adds crucial semantics: source_url can be direct or share links (Dropbox, Drive, OneDrive, SharePoint, Box) that auto-resolve; options.attendees names speakers; diarization is default true; output formats are enumerated and tied to the deliverable. This enriches the bare schema parameters.

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 opens with a specific verb+resource: 'Transcribe a video or audio file from a public HTTPS URL into a speaker-labelled transcript — ONE call does everything.' It clearly distinguishes itself from sibling conversion/transcoding tools by emphasizing speaker diarization, attendee labeling, and the broad range of output formats.

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?

It provides an explicit usage flow: pass source_url and output_format, receive a job_id, poll get_job_status, then get_download_url/get_output_content. It also gives fallback guidance and explicitly warns against writing custom ffmpeg/transcription code. However, it does not directly compare with transcribe_media or convert_from_url, leaving some ambiguity about when to prefer this tool.

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

A4.1/5.0

Disambiguation4/5

Each operation is split into clear source-specific variants (URL, uploaded file, inline content), and the descriptions go to great lengths to distinguish them. The only mild ambiguities are generic-sounding names like transcode_video versus transcode_from_url, and the similar get_job_status/get_workflow_status pair, but there is no true functional overlap.

Naming Consistency4/5

Most tools follow an imperative verb_noun pattern and use recurring suffixes like _from_url, _content, and _file, which creates a readable family structure. The pattern breaks slightly with uploaded-media variants named conform_media, transcode_video, and transcribe_media instead of a consistent _file or _uploaded suffix, and transcode_content is referenced in a description but missing from the actual tool list.

Tool Count4/5

17 tools is slightly above the ideal 3-15 range, but the server covers several related subdomains: document conversion, media transcode/transcribe/conform, job/workflow lifecycle, and wallet/billing. Given the need for URL, uploaded, and inline variants across multiple media types, the overall count is reasonable.

Completeness3/5

Core workflows are well covered: uploading, job submission, polling, and retrieving outputs all exist, and conversion has content/file/URL routes. However, get_upload_url explicitly tells agents to use transcode_content for inline media, but that tool does not exist, and there is no inline transcribe counterpart to convert_content, leaving a notable gap for sandboxed inline media jobs.

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