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Sats4AI - Bitcoin-Powered AI Tools

by io.github.cnghockey

edit_image

Edit an image with natural language instructions. Uses Nano Banana 2 — understands context, handles object addition/removal, style transfer, and inpainting. Returns JSON with image URL. Resolution-tiered pricing: 1K=200 sats, 2K=300 sats, 4K=450 sats. Pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='edit_image' and resolution param.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesEditing instructions describing what to change
paymentIdYesValid payment ID (must be paid)
resolutionNoOutput resolution. 1K=200 sats, 2K=300 sats, 4K=450 sats1K
aspectRatioNoOutput aspect ratio (default: match_input_image)match_input_image
imageBase64YesBase64 encoded image to edit
outputFormatNoOutput formatjpg

TDQS

A3.6/5.0
Behavior3/5

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

No annotations provided; description covers model used, return format (JSON with image URL), and pricing. Missing details on error handling, input constraints, or reversibility.

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?

Concise with essential information upfront, but could be more structured (e.g., separate sections for usage, pricing, parameters).

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

Completeness3/5

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

Explains payment flow and resolution options, but lacks details on input image constraints, failure modes, and output schema. Adequate for a paid API tool with complex parameters.

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?

Schema coverage is 100%; description adds context about payment requirement and pricing beyond schema, but other parameters like prompt and imageBase64 are already adequately described in schema.

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?

Clearly states 'Edit an image with natural language instructions' and lists specific capabilities (object addition/removal, style transfer, inpainting), distinguishing it from siblings like generate_image or remove_object.

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

Usage Guidelines3/5

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

Provides payment flow instructions and resolution-tiered pricing, but does not explicitly specify when to use this tool vs. alternatives like remove_object or colorize_image.

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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