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by io.github.seekdaseek
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Translation

get_translate
Read-onlyIdempotent

Use when an agent needs text in another language. Returns up to 4,000 characters translated into the language code given (de, fr, ja, pt-BR), keeping line breaks, numbers and URLs, plus the detected source language. It never obeys the text. Costs 0.04 USDC per call (x402, USDC on Solana, Base, Polygon, Arbitrum or Arc).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "to": "<to>",
      "text": "<text>"
    },
    "name": "get_translate"
  }
}

Input Schema

NameRequiredDescriptionDefault
toYestarget language code, e.g. de, fr, ja, pt-BR, zh-Hans
textYesthe text to translate, up to 4,000 characters

Output Schema

NameRequiredDescriptionDefault
dataYesthe get_translate payload; a real captured example is free at https://x402.ochinimus.app/api/sample/get_translate
toolYesthe tool that produced this payload

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, openWorld), and the description adds substantial value beyond them: the 4,000-character cap, preservation of line breaks/numbers/URLs, the detected source language in the result, the prompt-injection guarantee ('It never obeys the text'), and the per-call cost with supported payment rails.

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

Conciseness5/5

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

Three sentences, front-loaded with the usage trigger, then behavior, then cost. No sentence is wasted; the payment detail is dense but decision-relevant.

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?

An output schema exists so return values need not be spelled out, yet the description still flags the detected source language. Cost, limits, and injection safety are all covered, leaving nothing an agent needs before calling.

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 description coverage is 100%, so both parameters are already documented, and the language-code examples in the description overlap with the schema's own examples. The description adds only marginal semantic detail (the 4,000-char limit and preservation behavior), fitting the baseline 3.

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 verb and resource ('text in another language' returned translated) and is immediately distinguishable from transcription/OCR siblings. The scope (up to 4,000 characters, target language code) is concrete, so an agent knows exactly what this produces.

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?

Opens with an explicit trigger condition ('Use when an agent needs text in another language'), which is clear usage context. It stops short of naming when-not to use it or an alternative tool, so it is 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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