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Sats4AI - Bitcoin-Powered AI Tools

generate_music

Generate full songs (up to 6 min) with natural AI vocals, BPM/key control (99%+ accuracy), and 14+ section tags for precise arrangement. Uses Music-2.6 — orchestral and traditional instruments, style-aware mixing. Specify BPM, key, genre, mood in prompt. Returns MP3 URL. 500 sats per song. Pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='generate_music'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lyricsNoSong lyrics with section tags (up to 3,500 chars). Tags: [Intro], [Verse], [Pre Chorus], [Chorus], [Bridge], [Outro], [Solo], [Hook], [Drop], [Build Up], [Inst], [Interlude], [Transition], [Break], [Post Chorus]
promptYesMusic style with BPM, key, genre, mood, instruments (up to 2,000 chars). Example: 'E minor, 90 BPM, acoustic guitar ballad, male vocal'
bitrateNoAudio bitrate. Default: 256000
modelIdNoOptional. Omit for default model.
paymentIdYesValid payment ID (must be paid)
sample_rateNoAudio sample rate. Default: 44100
audio_formatNoOutput format. Default: mp3
is_instrumentalNoSet true for instrumental-only (no vocals). When true, prompt is required, lyrics are ignored.
lyrics_optimizerNoSet true to auto-generate lyrics from prompt when lyrics are empty.

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden and does well: it discloses the cost (500 sats per song), payment method (Bitcoin Lightning), return format (MP3 URL), and model capabilities. It lacks details on potential asynchronous behavior or failure modes, but the key operational traits are covered.

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?

The description is efficiently front-loaded with the core function, then provides additional context on model, usage, pricing, and payment. Every sentence contributes unique information with no redundancy or fluff, making it well-structured despite its length.

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

Completeness4/5

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

Given the complexity (9 params, no output schema), the description covers essential aspects: purpose, usage guidance, cost, payment flow, and output format. It does not explain error handling or whether the call is synchronous vs. async, but the sibling tools (await_result, check_job_status) partially mitigate that gap.

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

Parameters3/5

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

The schema has 100% coverage with detailed parameter descriptions, so the baseline is 3. The description adds a useful hint for the prompt parameter ('Specify BPM, key, genre, mood in prompt') and references section tags, but it does not go beyond what the schema already provides for other 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 clearly states the tool's function: 'Generate full songs (up to 6 min) with natural AI vocals, BPM/key control' and specifies the output as an MP3 URL. It distinguishes itself from sibling tools like generate_image, generate_text, and generate_video by focusing on music generation with unique capabilities like section tags and Music-2.6 model.

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 context on how to use the tool: 'Specify BPM, key, genre, mood in prompt' and mentions the payment prerequisite ('Requires create_payment with toolName="generate_music"'). However, it does not explicitly name alternatives or when not to use this tool, so it does not fully meet the 'explicit alternatives' criterion.

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.