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Sats4AI - Bitcoin-Powered AI Tools

by io.github.cnghockey
OverviewDiscussionsAdmin

transcribe_audio

Transcribe audio to text with WORD-LEVEL timestamps (timestamps:'word' returns per-word start/end times — subtitle alignment, karaoke captions, cutting video to speech) or segment timestamps. Uses Mistral Transcription — high-accuracy speech recognition that handles accents, background noise, and overlapping speakers. 13 languages: en, zh, hi, es, ar, fr, pt, ru, de, ja, ko, it, nl. Pass names (the people and places spoken in the audio) to help the speech model spell them right — free, same price. Priced per minute of audio; a 66-minute file works in one call. A result too large for one reply (word timestamps pass it after a few minutes of audio) comes back without segments, srt and vtt, with fullResultUrl: a free link to the whole result. Async — returns requestId, poll with check_job_status(jobType='transcription'), then get_job_result. 10 sats/min. Privacy: audio and transcripts are ephemeral — processed, returned, and discarded. Never persisted. Pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='transcribe_audio'.

Input Schema

NameRequiredDescriptionDefault
namesNoOptional: the names and places spoken in the audio (characters, people, towns), e.g. ["Siobhan", "Tadhg", "Chicoutimi"]. It helps the speech model spell them right. Free: the price does not change. We send up to 100At most 4000 characters of names. List names only: a name that sounds like an ordinary word can replace that word in the transcript ("Noone" for "noon"). If the model refuses the list, you still get the transcript, without it, and result.degraded includes "names".
diarizeNoIdentify different speakers (default false). Forces segment granularity upstream. If the speaker-label request fails, you still get the text, and result.degraded includes 'diarization'.
languageNoOptional. One of: detect (the default), en, zh, hi, es, ar, fr, pt, ru, de, ja, ko, it, nl. A locale tag (en-US) or the English name (French) also works; "auto" means detect.
paymentIdYesValid payment ID (must be paid)
timestampsNoTimestamp granularity in result.segments. 'word' returns per-word start/end times (subtitle alignment, karaoke captions, cutting video to speech). Default 'segment'. If the timestamp request fails, you still get the text, and result.degraded includes 'timestamps' (no segments, srt or vtt).
audioBase64YesBase64 encoded audio file
callback_idNoOptional correlation string echoed back in the webhook body. Max 128 chars.
callback_urlNoOptional HTTPS webhook we POST when the job finishes (HMAC-signed). Polling still works.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / language / description
      Previous value: -"Language code (e.g., 'en', 'es')"New value: +"Optional. One of: detect (the default), en, zh, hi, es, ar, fr, pt, ru, de, ja, ko, it, nl. A locale tag (en-US) or the English name (French) also works; \"auto\" means detect."
  2. Changed1 schema field changed
    • addedInput schema / properties / names
      Added value: +{
+  "description": "Optional: the names and places spoken in the audio (characters, people, towns), e.g. [\"Siobhan\", \"Tadhg\", \"Chicoutimi\"]. It helps the speech model spell them right. Free: the price does not change. We send up to 100At most 4000 characters of names. List names only: a name that sounds like an ordinary word can replace that word in the transcript (\"Noone\" for \"noon\"). If the model refuses the list, you still get the transcript, without it, and result.degraded includes \"names\".",
+  "items": {
+    "type": "string"
+  },
+  "type": "array"
+}
  3. Changed2 schema fields changed
    • changedInput schema / properties / diarize / description
      Previous value: -"Identify different speakers (default false). Forces segment granularity upstream."New value: +"Identify different speakers (default false). Forces segment granularity upstream. If the speaker-label request fails, you still get the text, and result.degraded includes 'diarization'."
    • changedInput schema / properties / timestamps / description
      Previous value: -"Timestamp granularity in result.segments. 'word' returns per-word start/end times (subtitle alignment, karaoke captions, cutting video to speech). Default 'segment'."New value: +"Timestamp granularity in result.segments. 'word' returns per-word start/end times (subtitle alignment, karaoke captions, cutting video to speech). Default 'segment'. If the timestamp request fails, you still get the text, and result.degraded includes 'timestamps' (no segments, srt or vtt)."
  4. Changed4 schema fields changed
    • addedInput schema / properties / callback_id
      Added value: +{
+  "description": "Optional correlation string echoed back in the webhook body. Max 128 chars.",
+  "type": "string"
+}
    • addedInput schema / properties / callback_url
      Added value: +{
+  "description": "Optional HTTPS webhook we POST when the job finishes (HMAC-signed). Polling still works.",
+  "type": "string"
+}
    • addedInput schema / properties / diarize
      Added value: +{
+  "description": "Identify different speakers (default false). Forces segment granularity upstream.",
+  "type": "boolean"
+}
    • addedInput schema / properties / timestamps
      Added value: +{
+  "description": "Timestamp granularity in result.segments. 'word' returns per-word start/end times (subtitle alignment, karaoke captions, cutting video to speech). Default 'segment'.",
+  "enum": [
+    "none",
+    "segment",
+    "word"
+  ],
+  "type": "string"
+}
  5. First observed

TDQS

A4.7/5.0
Behavior5/5

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

No annotations, so the description bears the full load and does so richly: pricing (10 sats/min, per-minute), ephemeral privacy guarantees, async requestId/polling flow, large-result truncation behavior with fullResultUrl, and degraded-result fallbacks. An agent knows cost, safety, and lifecycle before calling.

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?

Front-loaded with purpose and the high-value timestamp distinction, then pricing, size limits, async flow, privacy, and payment. Dense but every sentence carries actionable information with no filler.

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?

Async tool with no output schema, yet the description explains the return path (requestId, polling, get_job_result), truncation handling, and degraded outputs. Nothing an agent needs to call and consume results correctly is missing.

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 100%, so baseline is 3; the description adds value beyond the schema by noting 'names' is free and that word timestamps can trigger result truncation. Most parameter detail still lives in the schema, keeping it from a 5.

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?

States a specific verb and resource ('Transcribe audio to text') and immediately differentiates its output modes (word-level vs segment timestamps). The sibling transcribe_translate is implicitly distinguished by the pure transcription framing.

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?

Gives explicit when-to-use for the 'word' timestamp mode (subtitle alignment, karaoke captions, cutting video) and describes the prerequisite workflow (requires create_payment, poll with check_job_status, then get_job_result). It does not directly contrast with the transcribe_translate sibling, so not a full 5.

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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