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NiuTrans

MCP Translation Text

by NiuTrans
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The single translate_text tool has a clearly defined purpose that cannot be confused with any other tool in this server.

    Naming Consistency5/5

    With only one tool, naming consistency is inherently perfect. The tool follows a clear verb_noun pattern (translate_text) that would serve as a good model if more tools were added.

    Tool Count2/5

    A single tool for a translation server feels thin and incomplete. While translation is a focused task, even basic translation workflows might benefit from additional tools like language detection, batch translation, or translation history management. One tool is insufficient for proper coverage of the domain.

    Completeness2/5

    The server provides only a single translation endpoint with no supporting operations. There are significant gaps: no language detection tool, no batch translation capability, no way to list supported languages, and no management of translation history or preferences. The surface is severely incomplete for a translation service.

  • Average 4.3/5 across 1 of 1 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior4/5

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

    With no annotations provided, the description carries full burden and adds valuable behavioral context: it specifies the API provider (小牛翻译), mentions support for 450+ languages with alias handling, and details the return structure including raw API response. However, it doesn't cover potential limitations like rate limits 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.

    Conciseness5/5

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

    The description is well-structured and front-loaded: it starts with the core purpose, followed by key features, then detailed parameter and return explanations. Every sentence adds value with no redundancy, making it efficient and easy to parse.

    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?

    Given the tool's moderate complexity, 100% schema coverage, and the presence of an output schema (detailed in the Returns section), the description is complete. It covers purpose, parameters, return values, and behavioral aspects like language support, leaving no significant gaps for the agent.

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

    Parameters4/5

    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 adds meaningful context by explaining that parameters accept '语言代码或常见别名' and providing concrete examples (e.g., 'zh', '中文', 'chinese'), which clarifies usage beyond the schema's basic descriptions.

    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 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 distinguishes itself by mentioning the API provider and comprehensive language support, though no siblings exist for comparison.

    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?

    The description implies usage for text translation with language codes or aliases, but provides no explicit guidance on when to use this tool versus alternatives (e.g., other translation services or methods). Since no sibling tools exist, this is adequate but lacks broader context.

    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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