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Sats4AI - Bitcoin-Powered AI Tools

generate_video

Generate cinematic video from a text prompt. Uses MiniMax H3 — top 2 on the Artificial Analysis text-to-video leaderboard — with native audio. Async — returns requestId, poll with check_job_status. 768p or 2K (2K is upscaled from a 768p render), 5-15 seconds, priced per second by resolution (native audio free). Pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='generate_video' and duration, resolution params.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesText prompt describing the video
modelIdNoOptional. Omit for default model.
durationYesDuration in seconds (5-15)
paymentIdYesValid payment ID (must be paid)
resolutionNoOutput resolution: 768p or 2K (2K is upscaled from a 768p render). Omitting it yields 768p — ask for 2K explicitly, and quote it at create_payment too. Retired rungs still accepted: 480p/720p→768p, 1080p→2K.768p
generate_audioNoAccepted and IGNORED — H3 audio is native and always on, at no extra cost. There is no way to request a silent render.
reference_audioNoOptional https URL(s) of reference audio CLIPS, 2-15 seconds each (max 3, combined <=15s). Cite them in your prompt as "Audio 1", "Audio 2" or the model has no instruction to use them. ⚠ Audio cannot be sent alone — at least one reference_image must accompany it. For a music video, slice your track and pass one slice per shot. Free: does not change the price.
reference_imageNoOptional https URL of a reference still for character/style consistency across shots (accepts an array, max 3). Pass the URL from generate_image. Free: does not change the price.

TDQS

A5/5.0
Behavior5/5

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

Discloses async behavior (returns requestId for polling), payment requirement, and that audio is always on (no silent render). Explains retired resolution rungs and the constraint on reference_audio. This goes beyond the schema, which has no annotations, so the description fully carries behavioral transparency.

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?

While detailed, every sentence serves a purpose—pricing, async flow, parameter constraints, and behavioral notes. Structured logically from core purpose to specifics, with clear separate sentences per aspect. No fluff.

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 all necessary operational details: payment prerequisite, async polling, parameter constraints (audio requires image), resolution behavior, and retrieval flow. Given the complexity and absence of an output schema, the description is complete for an agent to invoke correctly.

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?

Adds crucial semantics for each parameter: resolution explains retired rungs and default, generate_audio is ignored, reference_audio requires a reference_image, and reference_image is for character consistency. The schema only lists definitions, so the description enriches meaning significantly.

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: 'Generate cinematic video from a text prompt.' Clearly distinguishes from siblings like generate_image and generate_music, and names the model (MiniMax H3) and key capabilities (native audio).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit usage instructions: async operation requiring create_payment, the need to pass reference_audio with at least one reference_image, and how resolution and audio options behave. Also notes that generate_audio is ignored and that 2K is upscaled from 768p, giving practical when-to-use guidance.

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.