Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP Translation Text translate 'Hello, how are you?' from English to Spanish"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
使用小牛翻译 API 将文本从 source 语种翻译到 target 语种。
支持 450+ 种语言代码，并可自动处理常见别名。返回结构包含译文和 API 原始响应。
Args:
text (str): 待翻译的原文文本，可以是任意长度的字符串。
source (str): 源语言代码或常见别名（例如 "zh"、"中文"、"chinese"）。
target (str): 目标语言代码或常见别名（例如 "en"、"英文"、"english"）。
Returns:
Dict[str, Any]: 包含以下字段的字典：
- source: 标准化后的源语言代码
- target: 标准化后的目标语言代码
- original_text: 原文
- translated_text: 译文
- raw: 小牛翻译 API 的原始响应数据
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | 待翻译的原文文本，可以是任意长度的字符串。 | |
| source | Yes | 源语言代码或常见别名（例如 "zh"、"中文"、"chinese"）。 | |
| target | Yes | 目标语言代码或常见别名（例如 "en"、"英文"、"english"）。 |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it specifies the external API integration ('小牛翻译 API'), mentions support for 450+ languages with alias handling, and describes the return structure. However, it doesn't cover rate limits, authentication needs, or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with a clear purpose statement upfront, followed by organized parameter and return value sections. Every sentence adds value without redundancy, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, 100% schema coverage, and presence of an output schema, the description is mostly complete. It covers purpose, parameters, and returns adequately, though it could benefit from mentioning authentication requirements or error scenarios for full completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 adds minimal value beyond the schema by briefly restating parameter purposes in the Args section, but doesn't provide additional semantic context like examples beyond what's already in the schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('使用小牛翻译 API 将文本从 source 语种翻译到 target 语种') with the resource (text) and scope (450+ languages with alias handling). It precisely defines what the tool does without being tautological or vague.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through language support details and API integration, but there are no explicit guidelines on when to use this tool versus alternatives. Since no sibling tools are provided, this is adequate but lacks proactive guidance about potential edge cases or limitations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
With only one tool, there is no possibility of confusion or overlap between tools. The single translate_text tool has a clearly defined purpose that is distinct by default.
The single tool name follows a clear verb_noun pattern (translate_text). With only one tool, consistency is inherently perfect as there are no other tools to compare against.
A single tool for a translation server feels thin and limited. While translation is a focused task, a more complete surface might include tools for language detection, batch translation, or translation history. The scope appears underdeveloped with just one tool.
The server covers basic text translation but has significant gaps. There's no tool for language detection, batch operations, or managing translations (e.g., getting supported languages, translation memory). This limits agent workflows and creates dead ends for more complex translation tasks.
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