A Model Context Protocol (MCP) server that provides AI applications with access to neural machine translation service, Amazon Translate for text translation, managed batch processing, and smart translation workflow management across 75+ languages.
- Text Translation: Real-time translation with custom terminology support
- Managed Batch Processing: End to End Large-scale document translation with S3 integration , monitoring and error analysis
- Language Detection: Automatic source language identification
- Custom Terminology: Domain-specific translation consistency
- Intelligent Workflows: Automated multi-step translation processes with workflow orchestration
- Error Analysis: Comprehensive error analysis for failed jobs
uvx awslabs.amazon-translate-mcp-server@latestpip install awslabs.amazon-translate-mcp-server
python -m awslabs.amazon_translate_mcp_server.server# AWS Configuration (required)
export AWS_REGION=us-east-1
export AWS_PROFILE=your-profile
# Optional Settings
export FASTMCP_LOG_LEVEL=INFO
export TRANSLATE_MAX_TEXT_LENGTH=10000Add to your Claude Desktop configuration:
{
"mcpServers": {
"amazon-translate": {
"command": "uvx",
"args": ["awslabs.amazon-translate-mcp-server@latest"],
"env": {
"AWS_REGION": "us-east-1",
"AWS_PROFILE": "default"
}
}
}
}translate_text- Translate text between languagesdetect_language- Identify source language automaticallyvalidate_translation- Quality assessment of translations
start_batch_translation- Process multiple documentsget_translation_job- Monitor job statuslist_translation_jobs- View all translation jobstrigger_batch_translation- Start job without monitoringmonitor_batch_translation- Monitor until completionanalyze_batch_translation_errors- Analyze failed jobs
list_terminologies- Browse custom terminology setscreate_terminology- Create domain-specific termsimport_terminology- Import from CSV/TMX filesget_terminology- Get terminology details
list_language_pairs- Show supported language combinationsget_language_metrics- View usage statistics
smart_translate_workflow- Automated translation with quality validationmanaged_batch_translation_workflow- Complete batch lifecycle managementlist_active_workflows- Monitor running workflowsget_workflow_status- Get workflow progress
# Translate text
translate_text(
text="Hello, world!",
source_language="en",
target_language="es"
)
# Returns: "¡Hola, mundo!"
# Auto-detect language
detect_language(text="Bonjour le monde")
# Returns: {"detected_language": "fr", "confidence_score": 0.99}# Start batch job
start_batch_translation(
input_s3_uri="s3://my-bucket/documents/",
output_s3_uri="s3://my-bucket/translated/",
data_access_role_arn="arn:aws:iam::123456789012:role/TranslateRole",
job_name="my-translation-job",
source_language="en",
target_languages=["es", "fr", "de"]
)# Automated translation with quality validation
smart_translate_workflow(
text="Hello, how are you?",
target_language="es",
quality_threshold=0.8
)Required IAM permissions:
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"translate:*",
"s3:GetObject",
"s3:PutObject",
"s3:ListBucket",
"sts:GetCallerIdentity"
],
"Resource": "*"
}
]
}- Authentication Errors: Ensure AWS credentials are configured
- Translation Failures: Check language pair support and text length limits
- Batch Job Issues: Verify S3 permissions and IAM role configuration
- Workflow Issues: Check workflow orchestrator in health check
# Clone and install
git clone https://github.com/awslabs/mcp.git
cd mcp/src/amazon-translate-mcp-server
uv venv && uv sync --all-groups
#mcp inspector
npx @modelcontextprotocol/inspector uv --directory <directory path to amazon-translate-mcp-server> run --module awslabs.amazon_translate_mcp_server.server
# Run tests
uv run --frozen pytest --cov --cov-branch --cov-report=term-missing
Apache License 2.0
Note: Requires AWS account with Amazon Translate access. AWS charges apply.