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Sats4AI - Bitcoin-Powered AI Tools

by io.github.cnghockey
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translate_text

Translate text across 119 languages with high accuracy. The target language picks the engine: GPT-OSS 120B by default, or a higher-scoring model (Gemini) where one measurably beats it. Auto-detects source language. Privacy-preserving: no data stored. Pricing: 1 sat per 1,000 characters on the standard engine, minimum 1 sat per request; a routed language costs more. GET /api/languages returns the exact price, engine and measured chrF for every language, and the 402 always quotes the real amount before you pay. 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.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • removedInput schema / properties / sourceLanguage / enum
      Removed value: -[
-  "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"
-]
    • changedInput schema / properties / targetLanguage / description
      Previous value: -"Target language (e.g., 'Spanish', 'French', 'Japanese')"New value: +"Target 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."
    • removedInput schema / properties / targetLanguage / enum
      Removed value: -[
-  "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"
-]
  2. First observed

TDQS

A3.6/5.0
Behavior4/5

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

With no annotations provided, the description carries the full behavioral burden and performs well: it discloses engine selection (GPT-OSS default, Gemini when better), privacy/no data storage, exact pricing per character, minimum payment, and the requirement to call create_payment. It is missing some edge-case behavior like rate limits or explicit error handling, but it reveals substantially more than the schema alone.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the central purpose and key behaviors like auto-detection and privacy. However, it is not concise: the 119-language list is a large block of tokens that largely duplicates the endpoint reference and schema note, and the pricing details are more verbose than necessary for calling the tool.

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?

The description covers a great deal: language coverage, engine selection, language formats, payment requirements, privacy, and pricing. But without an output schema, it never explicitly states the return value or how the translated text is delivered, and it omits guidance on choosing this tool over sibling tools. These gaps prevent it from being fully complete for a complex payment-gated tool.

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 description coverage is 100%, so the baseline is 3, but the description adds meaningful context: it maps targetLanguage to engine selection, explains that text drives the per-character price, and details the create_payment workflow. It also reinforces the auto-detection behavior for omitted sourceLanguage, going beyond the schema's basic parameter descriptions.

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 a specific verb and resource, so the tool's core function is unmistakable. It also mentions source auto-detection, which further clarifies the behavior. However, it does not explicitly distinguish itself from sibling translation tools like translate_rare_language, so it misses the differentiation needed for a 5.

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

Usage Guidelines3/5

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

The description provides clear usage context: it translates text, auto-detects the source language, and requires a payment created via create_payment. It also gives helpful instruction about omitting sourceLanguage and using language names or ISO codes. Yet it gives no explicit guidance on when not to use it versus sibling alternatives such as translate_rare_language or multilingual_ask.

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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