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.

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

A4/5.0

Disambiguation4/5

Tools are largely distinguishable by their action and input source (content/file/url), but the multiple convert_* and transcode/transcribe variants overlap in purpose, and get_job_status vs get_workflow_status could cause confusion. Descriptions are thorough enough to guide selection, but the boundaries between similar actions are not always crisp.

Naming Consistency4/5

All names use snake_case with a verb-noun pattern (get_, convert_, transcode_, transcribe_, submit_, cancel_), which is consistent. However, the variant naming is not perfectly parallel: convert_content/file/from_url use a source suffix, whereas transcode_video vs transcode_from_url and transcribe_media vs transcribe_from_url mix output type and source, creating minor inconsistency.

Tool Count4/5

At 15 tools, the server is at the upper end of a well-scoped set. Each tool supports a distinct input/output route or workflow function, but the count is slightly heavy due to multiple variants of similar operations. It remains appropriate for the breadth of conversion, transcoding, transcription, workflow, and wallet features.

Completeness3/5

The core job lifecycle (submit, poll, retrieve output) is well covered, and workflows have submit/status/cancel. However, get_upload_url references a transcode_content tool that does not exist in the toolset, and there is no way to cancel a single job (only full workflows). This leaves gaps for inline media transcoding in sandboxed environments and granular job control.

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