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Sats4AI - Bitcoin-Powered AI Tools

by io.github.cnghockey

translate_rare_language

Translate into 452 languages, 251 of them NOT supported by ChatGPT, Claude or Gemini (29 of those 251 measured at fair quality or better against human references) — including Bhojpuri (~50M speakers), Maithili (~34M), Egyptian Arabic (~100M), Moroccan Arabic (~30M), Chhattisgarhi, Magahi, Manipuri, Kashmiri, Shan, Kachin, Awadhi, Tamazight, Crimean Tatar, Quechua, Nuer, Sango, plus indigenous and minority languages with no callable API anywhere. Runs MADLAD-400 (Apache-2.0). QUALITY VARIES AND IS PUBLISHED PER LANGUAGE: every language carries a measured tier — good (chrF++ >= 45 vs human reference translations), fair (32-45), unverified (no benchmark exists, untested, may be poor), experimental (known weak). The response repeats the tier so you can judge how much to trust it. GET https://sats4ai.com/api/l402/translate-rare-language for the full language list with tiers, or GET /api/languages. Unsupported languages are rejected BEFORE payment. For mainstream languages use translate_text instead — it is cheaper and more fluent. Priced 50 sats base + 0.002 sats/char (GPU). Pay with Bitcoin Lightning — no API key or signup. Requires create_payment with toolName='translate_rare_language'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesText to translate. Max 6000 characters.
paymentIdYesValid payment ID (must be paid). Price = 50 sats + 0.002/char.
targetLanguageYesMADLAD language code ('mag', 'arz', 'bho', 'hne') or English name ('Magahi', 'Egyptian Arabic'). Rejected before payment if unsupported.

TDQS

A4.8/5.0
Behavior5/5

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

With no annotations, the description fully discloses quality tiers (good/fair/unverified/experimental), model used (MADLAD-400), and that response includes the tier. No hidden behaviors.

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 thorough and well-organized, with key information front-loaded. A few minor redundancies (e.g., listing many languages) but generally every sentence adds value.

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 3 required parameters and no output schema, the description covers all essential aspects: quality, payment, prerequisites, and fallback tool. No gaps identified.

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?

All three parameters have descriptions in the schema (100% coverage). The description adds value by clarifying text length limit, pricing formula per character, and acceptable language identifiers (codes or English names).

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 it translates into rare languages, specifying 452 languages, many not supported by other chatbots. It explicitly distinguishes from mainstream translation tools and provides concrete language examples.

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 recommends using 'translate_text' for mainstream languages (cheaper, more fluent). Describes payment flow via Bitcoin Lightning. States unsupported languages are rejected before payment.

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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