ZapifyAPI MCP Server

translate_text

Translate any text into 50+ languages by specifying target language code and optional source code.

Instructions

Translate text between languages. Supports 50+ languages.

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>",
      "to": "<to>"
    },
    "name": "translate_text"
  }
}

Input Schema

Name	Required	Description
`to`	Yes	Target language code
`from`	No	Source language code (or "auto")
`text`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 3, 2026

TDQS

B3.1/5.0

Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only mentions language support (50+ languages) but does not disclose behavior such as auto-detection, error handling, rate limits, or whether the operation is safe and reversible. This is minimal and insufficient for a tool with no annotation safety signals.

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?

The description is two sentences long and every word earns its place. It is front-loaded with the core purpose and adds a brief capability claim. There is zero fluff or redundancy, making it highly efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 3 parameters, no output schema, and no annotations, the description is too sparse. It does not explain expected input formats, output structure, or any constraints. The agent would need to infer too much, making the tool risky to invoke without additional information.

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

Parameters2/5

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

The schema describes 'to' and 'from' but leaves 'text' undocumented (67% coverage). The description adds no parameter meaning beyond the schema; it does not clarify that 'text' is the content to translate or explain language code formats. It fails to compensate for the underscore-documented 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 tool's function: 'Translate text between languages.' The verb 'translate' and resource 'text' are specific, and the phrase 'between languages' adds scope. It distinguishes from all sibling tools, 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 Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. There are no mentions of exclusions, preferred use cases, or distinctions from other text-processing tools like analyze_sentiment or extract_keywords. The agent is left without context for tool selection.

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