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Sats4AI - Bitcoin-Powered AI Tools

by io.github.cnghockey

transcribe_translate

Compound endpoint — one payment turns audio in any of 13 source languages into both a transcript AND a translation in any of 119 target languages. Perfect for WhatsApp voice messages in a language you don't speak (Yoruba → English), or recording a meeting in another language and reading it in yours. Auto-detects source if omitted. Async — returns requestId, poll with check_job_status(jobType='transcribe-translate'). Flat price covers STT + translation. Cheaper than calling transcribe_audio + translate_text separately for typical voice messages. Pay with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='transcribe_translate'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paymentIdYesValid payment ID (must be paid)
audioBase64YesBase64-encoded audio file
sourceLanguageNoOptional — auto-detected if omitted. Accepts ISO-639 codes for the 13 STT languages: en, zh, hi, es, ar, fr, pt, ru, de, ja, ko, it, nl. NOTE: only these 13 are transcribable — a wrong hint (or audio in another language) yields a garbled transcript that is still billed as success. Omit to auto-detect and verify the transcript before trusting the translation.
targetLanguageYesTarget language — English name (e.g. 'Spanish') or ISO-639 code (e.g. 'es', 'en-US'). 119 languages supported.

TDQS

A4.4/5.0
Behavior4/5

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

Despite no annotations, description discloses async polling, payment requirement, auto-detection, and a warning about language inaccuracies. Covers key behaviors well.

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?

Front-loaded with main purpose, each sentence adds value, though slightly verbose. No redundancy.

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?

Covers prerequisites, async result handling, pricing comparison, and language limitations. Complete for a complex tool without output schema.

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%, but description adds context: explains paymentId origin, lists allowed source language codes with warning, and clarifies target language formats.

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?

Description clearly states it transcribes and translates audio from 13 source languages to 119 target languages, distinguishes from sibling tools transcribe_audio and translate_text by being a compound endpoint and cheaper.

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?

Provides clear use cases (WhatsApp voice messages, meeting recordings) and contrasts with separate calls, but lacks explicit when-not-to-use instructions.

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

Most tools have distinct purposes, but there is some overlap, especially among call tools (ai_call, place_call, open_voice_bridge) and image generation/editing tools (generate_image, edit_image, animate_image). Descriptions help differentiate, but an agent might still select the wrong one.

Naming Consistency4/5

The vast majority of tools follow a verb_noun pattern (e.g., generate_image, send_sms). A few exceptions exist (await_result, check_job_status, epub_to_audiobook) but the overall pattern is strong and predictable.

Tool Count3/5

With 50 tools, the server is very extensive. While each tool earns its place given the broad scope of AI services, the count feels high and could overwhelm agents, making selection less efficient.

Completeness5/5

The tool surface is remarkably comprehensive, covering generation, editing, conversion, communication, async management, payments, and error handling. There are no obvious gaps for the stated Bitcoin-powered AI toolkit purpose.

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