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Sats4AI - Bitcoin-Powered AI Tools

by io.github.cnghockey

translate_text

Translate text across 119 languages with high accuracy. Uses GPT-OSS 120B — top scorer in our multilingual translation evals with strong low-resource support. Auto-detects source language. Privacy-preserving: no data stored. Pricing: 1 sat per 1,000 characters, minimum 1 sat per request. Language parameters accept English names ('Spanish', 'Chinese (Simplified)') or ISO-639 codes / locale tags ('es', 'en-US', 'pt-BR', 'zh-Hans'). Supported languages: Afrikaans, Albanian, Amharic, Arabic, Armenian, Assamese, Azerbaijani, Basque, Belarusian, Bengali, Bosnian, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese (Simplified), Chinese (Traditional), Corsican, Croatian, Czech, Danish, Dari, Dutch, English, Esperanto, Estonian, Farsi, Fijian, Filipino, Finnish, French, Frisian, Galician, Georgian, German, Greek, Guarani, Gujarati, Haitian Creole, Hausa, Hawaiian, Hebrew, Hindi, Hmong, Hungarian, Icelandic, Igbo, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Kinyarwanda, Korean, Kurdish, Kyrgyz, Lao, Latvian, Lingala, Lithuanian, Luganda, Luxembourgish, Macedonian, Malagasy, Malay, Malayalam, Maltese, Maori, Marathi, Mongolian, Nepali, Norwegian, Occitan, Odia, Pashto, Polish, Portuguese, Punjabi, Romanian, Romansh, Russian, Samoan, Scots Gaelic, Serbian, Sesotho, Setswana, Shona, Sindhi, Sinhala, Slovak, Slovenian, Somali, Spanish, Sundanese, Swahili, Swedish, Tajik, Tamil, Tatar, Telugu, Thai, Tigrinya, Tongan, Turkish, Turkmen, Ukrainian, Urdu, Uzbek, Vietnamese, Welsh, Wolof, Xhosa, Yiddish, Yoruba, Zulu. Pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='translate_text' and prompt (the text to translate).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesText to translate
modelIdNoOptional. Translation model is selected automatically.
paymentIdYesValid payment ID (must be paid)
sourceLanguageNoSource language (auto-detected if omitted). NOTE: only checked for being a known language, not against your text — a wrong but valid value (e.g. 'German' for Spanish text) is accepted and silently mistranslates with no error. Omit it to let auto-detect work.
targetLanguageYesTarget language — English name ('Spanish', 'Chinese (Simplified)') or ISO-639 code / locale tag ('es', 'pt-BR'). 119 supported; full list at the GET /api/l402/translate-text endpoint.

TDQS

A4/5.0
Behavior5/5

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

With no annotations, the description fully discloses behavioral traits: model selection (GPT-OSS 120B), auto-detection of source language, privacy policy (no data stored), pricing (1 sat per 1,000 chars), language format support, and a critical warning about sourceLanguage causing silent mistranslation if set incorrectly. This is comprehensive transparency.

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 front-loaded with the core purpose and model, followed by key details in a logical order. It is somewhat lengthy due to the language list and pricing details, but every sentence serves a purpose for a tool that requires payment and has many parameters. Could be slightly trimmed but remains 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 the tool's complexity (5 parameters, payment requirement, 119 languages, no output schema), the description covers essential aspects: how to pay, language formats, auto-detection, and a critical caveat. It does not explicitly state the return value, but for translation it is intuitive. Overall quite complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although schema coverage is 100%, the description adds significant meaning: explains pricing per character, language format options (English names, ISO codes, locale tags), notes that modelId is optional and auto-selected, and warns about the sourceLanguage parameter leading to silent mistranslation if improperly set. This goes well beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Translate text across 119 languages with high accuracy,' specifying the verb (translate), resource (text), and scope (119 languages, GPT-OSS 120B model). However, it does not differentiate from sibling tools like transcribe_translate or translate_rare_language, reducing clarity relative to alternatives.

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

Usage Guidelines2/5

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

The description provides extensive details about usage (payment, language codes) but offers no guidance on when to use this tool versus siblings or when not to use it. It does not mention alternatives or conditions for preferring this tool over others.

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