A
licenseB
qualityD
maintenanceEnables MCP-compatible AI applications to translate text between languages with automatic language detection, contextual hints, custom instructions, and multi-language support.
1
821 npm
MIT
dualtran-mcp
by seigwen
Related Servers
Alternatives to dualtran-mcp
No user-submitted related servers found.
Related Servers
- AlicenseBqualityDmaintenanceEnables MCP-compatible AI applications to translate text between many languages, with automatic source-language detection, contextual hints, and custom translation instructions.1821 npmMIT
- AlicenseAqualityDmaintenanceProvides text translation capabilities powered by NiuTrans API with support for 455+ languages. Includes translation tools and language catalog resources for seamless integration with MCP clients.18MIT
- AlicenseBqualityDmaintenanceEnables AI applications to perform multi-language translations with automatic language detection, contextual hints, and custom instructions through an MCP-compatible interface.1821 npmMIT
- AlicenseAqualityDmaintenanceEnables MCP-compatible AI applications to translate text through the Lara Translate API, with automatic language detection, context-aware translations, and custom instructions.1821 npmMIT
- AlicenseNot gradedqualityDmaintenanceEnables translation and text rephrasing using DeepL's API through a cloud-deployed MCP server. Provides translation between multiple languages, text rephrasing, and language detection capabilities with professional deployment features.MIT
TDQS
A3.7/5.0
Scored across 3 tools
Disambiguation5/5
The three tools have clearly distinct purposes: single-text translation, batched translation, and engine introspection. An agent can easily select the right one with no overlap.
Naming Consistency5/5
All names follow a consistent snake_case verb_noun pattern (translate_text, translate_batch, list_engines). The convention is predictable and readable.
Tool Count4/5
Three tools is at the low end but reasonable for a narrow translation-focused server. Each tool earns its place, though the surface is slightly thin.
Completeness4/5
Core translation workflows (single, batch) plus engine introspection are covered. Minor gaps like explicit per-call engine selection or language detection could be worked around.