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by io.github.cnghockey
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transcribe_translate

Compound endpoint — one payment turns audio in any of 13 source languages into both a transcript AND a translation in any of 119 target languages. Perfect for WhatsApp voice messages in a language you don't speak (Yoruba → English), or recording a meeting in another language and reading it in yours. Auto-detects source if omitted. Pass names (the people and places spoken in the audio) to help the speech model spell them right — free, same price. Async — returns requestId, poll with check_job_status(jobType='transcribe-translate'). Flat price covers STT + translation. Cheaper than calling transcribe_audio + translate_text separately for typical voice messages. Pay with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='transcribe_translate'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
namesNoOptional: the names and places spoken in the audio (characters, people, towns), e.g. ["Siobhan", "Tadhg", "Chicoutimi"]. It helps the speech model spell them right. Free: the price does not change. We send up to 100At most 4000 characters of names. List names only: a name that sounds like an ordinary word can replace that word in the transcript ("Noone" for "noon"). If the model refuses the list, you still get the transcript, without it, and result.degraded includes "names".
paymentIdYesValid payment ID (must be paid)
audioBase64YesBase64-encoded audio file
sourceLanguageNoOptional — auto-detected if omitted. Accepts ISO-639 codes for the 13 STT languages: en, zh, hi, es, ar, fr, pt, ru, de, ja, ko, it, nl. NOTE: only these 13 are transcribable — a wrong hint (or audio in another language) yields a garbled transcript that is still billed as success. Omit to auto-detect and verify the transcript before trusting the translation.
targetLanguageYesTarget language — English name (e.g. 'Spanish') or ISO-639 code (e.g. 'es', 'en-US'). 119 languages supported.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / names
      Added value: +{
+  "description": "Optional: the names and places spoken in the audio (characters, people, towns), e.g. [\"Siobhan\", \"Tadhg\", \"Chicoutimi\"]. It helps the speech model spell them right. Free: the price does not change. We send up to 100At most 4000 characters of names. List names only: a name that sounds like an ordinary word can replace that word in the transcript (\"Noone\" for \"noon\"). If the model refuses the list, you still get the transcript, without it, and result.degraded includes \"names\".",
+  "items": {
+    "type": "string"
+  },
+  "type": "array"
+}
  2. First observed

TDQS

A4.3/5.0
Behavior5/5

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

No annotations are provided, so the description carries the full burden. It discloses async behavior (returns requestId, poll with check_job_status), payment prerequisite (create_payment with toolName), flat pricing, and a critical billing caveat: a wrong sourceLanguage hint yields a garbled transcript still billed as success. It also notes names are free and may degrade if refused.

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?

Front-loads the compound purpose, then covers examples, async, pricing, and payment. Slightly repetitive on pricing ('flat price covers STT + translation' and 'cheaper than...'), but every sentence carries useful context.

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 annotations and no output schema, the description covers the critical workflow, language limits, async polling, and billing caveats. It does not specify the final result shape or field names, leaving some retrieval ambiguity.

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

Parameters3/5

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

Schema coverage is 100%, so parameters are already well documented. The description repeats key constraints (auto-detect source, 119 target languages, names free) but adds no parameter syntax or semantics beyond what the schema provides.

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?

States a specific compound operation: converts audio into both a transcript and a translation across 13 source and 119 target languages. Distinguishes from siblings by naming transcribe_audio + translate_text alternatives and the check_job_status/create_payment workflow.

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?

Provides clear use cases (WhatsApp voice messages, meeting recordings) and a comparative alternative (cheaper than separate calls for typical voice messages). Lacks explicit when-not-to-use exclusions, so not a full 5.

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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