MyClaw Toolkit MCP Server

ai_translate

Translate text between languages with automatic source language detection. Specify the target language to get AI-powered translations.

Instructions

Translate text between languages using AI

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": {
      "text": "<text>"
    },
    "name": "ai_translate"
  }
}

Input Schema

Name	Required	Description	Default
`text`	Yes	Text to translate
`from`	No	Source language (auto for auto-detect)	auto
`to`	No	Target language	en

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.1Jun 23, 2026

TDQS

B3.4/5.0

Behavior2/5

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

With no annotations, the description carries the full burden. It reveals only that the translation uses AI, but does not disclose if it is read-only, supported languages, limits, or response format. This is insufficient for a transparent behavioral disclosure.

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 very concise (one short sentence) with no extraneous words. While it could benefit from more detail, it is not verbose and effectively communicates the core function.

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 the basic purpose but lacks details on output format, language coverage, or any constraints. Given the tool's simplicity and lack of output schema, a score of 3 reflects adequate but not comprehensive completeness.

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 the baseline is 3. The description does not add any extra meaning beyond what the schema already provides for each parameter.

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 the action (translate text) and the resource (between languages using AI). It effectively distinguishes this tool from its siblings, none of which are translation-related.

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?

No explicit when-to-use or when-not-to-use guidance is given. However, since there are no competing translation siblings, the implied usage is clear. A score of 3 reflects the absence of explicit alternatives or prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.