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Sats4AI - Bitcoin-Powered AI Tools

receive_fax

When you're expecting a fax back — bank confirmation, court filing, signed document — open a 24h receive window at our shared number +1 320 299 1523. Matched by caller ID (last 10 digits of the sender), delivered to your email as soon as it arrives. Optional OCR add-on (+200 sats) returns a searchable text file alongside the PDF — useful for feeding the content to an agent or archiving. Optional callback_url POSTs an HMAC-signed webhook on delivery so your agent doesn't have to poll. No refund if no fax arrives within the window (prevents subscription squatting). If OCR fails, an LNURL-withdraw for 200 sats is included in the delivery email for partial refund. Pay with Bitcoin Lightning — no dedicated fax number rental, no monthly subscription, no account.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ocrNoAdd OCR text extraction (+200 sats). Default: false.
emailYesEmail address to deliver the fax PDF to
paymentIdYesValid payment ID (must be paid)
fromNumberYesExpected sender fax number in E.164 format (matched by last 10 digits of caller ID)
callback_idNoOptional opaque correlation string (max 128 chars). Echoed in the webhook body.
callback_urlNoOptional HTTPS webhook URL. POSTed (HMAC-signed) when fax is delivered. Public HTTPS only — no loopback/RFC1918.

TDQS

A4.5/5.0
Behavior5/5

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

Despite only the 'openWorldHint' annotation indicating external interaction, the description richly discloses behavioral traits: 24-hour receive window, caller ID matching (last 10 digits), email delivery, optional OCR (+200 sats) with partial refund on failure, optional HMAC-signed webhook callback, and the no-refund policy to prevent squatting. Payment via Lightning is also noted. These details go well beyond the annotation's minimal information.

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?

The description is a single paragraph of ~120 words, efficiently covering the main purpose, constraints, optional features, and policies. It is front-loaded with the primary use case. While dense, every sentence adds necessary information, making it concise for the tool's complexity. Minor improvement could be bullet-style structure, but current form is clear.

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 no output schema, the description effectively explains the delivery process (email, OCR, webhook) and constraints. It covers the immediate action (opening a window) and post-delivery behavior. However, it does not describe the immediate tool response (e.g., a confirmation or window ID), which may be needed for subsequent steps. Still, the description is fairly complete for a tool with offline delivery.

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 coverage is 100% with each parameter having a description. The description adds value by explaining the OCR add-on cost, the HMAC-signed webhook behavior, and the caller ID matching for fromNumber. It contextualizes the parameters within the use case. However, it does not add significant detail beyond the schema for email, paymentId, and callback_id, keeping the score just above baseline.

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 purpose: 'receive a fax' at a shared number, with delivery to email. It specifies the use case ('when expecting a fax back') and distinguishes from sending (sibling 'send_fax' exists). The verb 'receive' and resource 'fax' are specific, and the description contrasts with dedicated number rental, making purpose unambiguous.

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?

The description provides explicit context for when to use the tool ('When you're expecting a fax back') and mentions the 24-hour window and no-refund policy. It does not directly state when not to use it, but the context is clear. Alternative tools (like send_fax) exist among siblings, but no explicit exclusion is given. The guidance is helpful but lacks explicit alternatives.

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.