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Sats4AI - Bitcoin-Powered AI Tools

by io.github.cnghockey

send_fax

Destructive

When your task requires a paper-trail on the other end — loan paperwork to a bank, signed contract to a notary, booking confirmation to a hotel in Japan — send a fax to any number worldwide. Two modes: 'pdf' (fetch from public URL) or 'text' (we format typed text into a PDF locally). Text/cover support Latin (incl. Central European, Vietnamese), Greek, Cyrillic, Japanese, Korean, Chinese (Simplified + Traditional), Thai, Hindi, Georgian, Armenian, Amharic; RTL scripts (Arabic/Hebrew) and emoji are rejected BEFORE your payment is consumed. Optional cover page. Pricing: 500 sats for up to 10 pages, +50 sats per additional page. Max 350 pages / 50 MB. Pass 'pages' to create_payment as 'quantity' to get the right invoice. Pay with Bitcoin Lightning — no fax machine, no phone line, no telecom account.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYes'pdf' = send PDF from pdfUrl. 'text' = generate PDF from typed text.
textNoRequired for mode=text: message text to format as PDF
pagesNoExpected page count (1-350). Used for pricing. Pass same value to create_payment as 'quantity'.
pdfUrlNoRequired for mode=pdf: public HTTPS URL returning application/pdf
coverTextNoOptional cover page text (mode=pdf only, adds 1 page)
paymentIdYesValid payment ID (must be paid)
phoneNumberYesDestination fax number in E.164 format (e.g. +14155550100)

TDQS

A4.6/5.0
Behavior4/5

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

Discloses modes, language support, rejection behavior, pricing, max pages/size, and payment necessity. Annotations already indicate destructiveHint=true (consumes credit), and description adds context about what gets rejected pre-payment. Could mention retry or status behavior but overall transparent.

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 substantial but front-loaded with use cases. Every sentence adds value (pricing, limitations, mode details). Could be slightly more structured (e.g., bullet points) but is not verbose.

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?

Given 7 parameters, complex dual-mode behavior, language support, and pricing details, the description covers all necessary aspects. No output schema exists, but behavior and constraints are fully explained, enabling correct invocation.

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 covers all parameters with descriptions. Description adds value by specifying phone number format (E.164), explaining pages as quantity for create_payment, and clarifying mode differences (pdfUrl vs text). Provides more context than schema alone.

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 uses specific verb 'send' and resource 'fax', lists use cases (loan paperwork, signed contract, booking confirmation), and distinguishes two modes (pdf/text). Clearly differentiates from sibling receive_fax.

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 (need paper trail, worldwide), provides mode selection criteria, lists rejected content (RTL, emoji) before payment, details pricing and prerequisite paymentId, and directs to create_payment for proper invoicing.

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