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Amazon Translate MCP Server

Amazon Translate is a highly scalable, neural machine translation service provided by Amazon Web Services (AWS) that enables developers to translate text, documents, and websites from a source language to one of dozens of supported target languages.

Quick Start Summary

The Amazon Translate MCP server is a Model Context Protocol bridge that connects AI assistants — including Claude Desktop, Cursor, Windsurf, and VS Code Copilot — to the Amazon Translate API through natural language. It exposes 10 API endpoints as callable tools, such as CreateParallelData, DeleteParallelData, DeleteTerminology, and more. No authentication is required — setup takes approximately 30 seconds. The server uses STDIO transport and can be installed by running npx -y @mcp/amazonaws-com-translate. This integration is sourced from the auto Amazon Translate OpenAPI specification (v2017-07-01) and has a quality score of 46/99 (fair documentation coverage).

10Endpointstools mapped
NoneAuthopen access
46/99Qualityfair
~30 secSetupno auth

Server Details

Category
Developer Tools
Authentication
None
Endpoints
10 operations
Transport
STDIO
Spec Version
v2017-07-01
Install Command
npx -y @mcp/amazonaws-com-translate

Environment Variables

AMAZON_TRANSLATE_API_KEY

Example: your_amazon_translate_api_key

Top Endpoints

POST
/#X-Amz-Target=AWSShineFrontendService_20170701.CreateParallelData

CreateParallelData

POST
/#X-Amz-Target=AWSShineFrontendService_20170701.DeleteParallelData

DeleteParallelData

POST
/#X-Amz-Target=AWSShineFrontendService_20170701.DeleteTerminology

DeleteTerminology

POST
/#X-Amz-Target=AWSShineFrontendService_20170701.DescribeTextTranslationJob

DescribeTextTranslationJob

POST
/#X-Amz-Target=AWSShineFrontendService_20170701.GetParallelData

GetParallelData

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📖 Detailed MCP Integration Guide

A technical breakdown of capabilities, agent workflows, and security/configuration best practices.

Capabilities & Use Cases
Amazon Translate is a highly scalable, neural machine translation service provided by Amazon Web Services (AWS) that enables developers to translate text, documents, and websites from a source language to one of dozens of supported target languages. Beyond basic text-to-text translation, its core capabilities include real-time translation for streaming data, batch translation for large document sets via Amazon S3, and specialized features for custom terminology and parallel data to ensure brand names, product descriptions, and sensitive content are translated consistently and accurately. This service is engineered for enterprise-grade applications, powering global customer support systems, enabling real-time multilingual communication in applications, facilitating content localization for international markets, and aiding in the analysis of multilingual datasets for business intelligence. It is a foundational component for any application requiring high-accuracy, low-latency translation at scale, abstracting away the complexities of maintaining and training underlying translation models.
🤖AI Agent Value
When exposed as tools through the Model Context Protocol (MCP) to AI coding assistants such as Claude Desktop, Cursor, or Cline, the Amazon Translate API gains a new dimension of utility. The MCP server acts as a bridge, allowing an AI agent to dynamically invoke specific translation operations based on a developer's natural language instructions within their integrated development environment (IDE). This transforms the API from a static backend service into a conversational, actionable toolset. The AI can programmatically manage the lifecycle of translation resources, such as querying the ListLanguages endpoint to provide the developer with the most current set of supported language codes for an application they are building. It can also orchestrate more complex, stateful workflows by sequentially calling endpoints, for instance, by first retrieving a custom terminology list with GetTerminology and then using it to translate a product catalog via a batch job, all through a single high-level command.
💬Example Workflows
This integration unlocks dynamic, AI-assisted workflows that significantly accelerate development and content management tasks. A developer can instruct their AI agent to perform operations like: "Check the status of my ongoing translation job for the French support tickets," prompting the AI to call DescribeTextTranslationJob and summarize the progress. For localization engineering, one could command: "List all my custom parallel data resources for the English to Spanish pair, then retrieve the detailed content of the one labeled 'legal-docs'," causing the AI to execute ListParallelData followed by GetParallelData and present the results in a clear format. Furthermore, the AI can assist in setup and maintenance tasks, such as "Help me prepare to import a new terminology list for brand names; list the available resources first," guiding the developer through a secure and correct configuration process by interactively using the ListParallelData and ImportTerminology endpoints.
🛡️Security & Auth
Critical to the secure and effective use of this API is a clear understanding of its authentication and authorization model, which, while not specified in the endpoint list, is intrinsically tied to AWS IAM. All Amazon Translate API calls must be authenticated using AWS Signature Version 4, requiring valid AWS access keys. Developers must strictly adhere to the principle of least privilege when configuring IAM roles or users for the MCP server, granting only the specific translate permissions (e.g., translate:TranslateText, translate:ImportTerminology) needed for the intended use case. Credentials should never be hardcoded; instead, the MCP server should be configured to use the default AWS credential provider chain, which securely retrieves credentials from environment variables, instance profiles, or configured AWS profiles. For enhanced security, organizations should utilize IAM policies to restrict access to specific source/target language pairs or to protect sensitive terminology resources, ensuring the AI agent operates within a carefully defined security boundary.

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