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Sats4AI - Bitcoin-Powered AI Tools

by io.github.cnghockey

analyze_image

Analyze and describe image content, answer visual questions, extract information from screenshots or photos. Uses Qwen VL — multimodal vision-language model with strong OCR, chart reading, and spatial reasoning. 21 sats per image. Pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='analyze_image'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesQuestion or analysis prompt for the image
modelIdNoOptional. Omit for default model.
paymentIdYesValid payment ID (must be paid)
imageBase64YesBase64 encoded image to analyze. Single images only — for PDFs or multi-page document text use extract_document (a PDF sent here is treated as an image and may return a wrong/hallucinated result with no error).

TDQS

A4.2/5.0
Behavior4/5

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

No annotations provided, so description carries full burden. Discloses model (Qwen VL), capabilities (OCR, chart reading, spatial reasoning), pricing, and a critical behavior warning about PDFs causing hallucinations. Missing info on async vs sync and error handling.

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?

Description is concise with only necessary sentences. Front-loads the core purpose. Minor redundancy: payment info could be separate but not detrimental.

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?

Covers purpose, usage, behavioral traits, and payment. Missing clarity on return format (presumably text) and whether results are synchronous. With 4 params and no output schema, some gaps remain.

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 covers all 4 parameters with detailed descriptions, so baseline is 3. Description reinforces the PDF warning for imageBase64 parameter but adds no new semantic value beyond 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?

Description clearly states the tool analyzes images, answers visual questions, and extracts information. It distinguishes from sibling extract_document by explicitly noting single images only.

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?

Explicitly states when to use (visual analysis) and when not (PDFs/multi-page documents, recommending extract_document). Also mentions payment requirement and cost (21 sats per 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.

Resources