AWS Athena MCP Server
Enables execution of SQL queries against AWS Athena databases with schema discovery, query status management, and result retrieval through a standardized Model Context Protocol interface.
README
AWS Athena MCP Server
A simple, clean MCP (Model Context Protocol) server for AWS Athena integration. Execute SQL queries, discover schemas, and manage query executions through a standardized interface.
✨ Features
- Simple Setup - Get running in under 5 minutes
- Clean Architecture - Modular, well-tested, easy to understand
- Essential Tools - Query execution and schema discovery
- Type Safe - Full type hints and Pydantic models
- Async Support - Built for performance with async/await
- Good Defaults - Works out of the box with minimal configuration
🚀 Quick Start
1. Install
# From PyPI with uv (recommended for Claude Desktop)
uv tool install aws-athena-mcp
# From PyPI with pip
pip install aws-athena-mcp
# Or from source
git clone https://github.com/ColeMurray/aws-athena-mcp
cd aws-athena-mcp
pip install -e .
2. Configure
Set the required environment variables:
# Required
export ATHENA_S3_OUTPUT_LOCATION=s3://your-bucket/athena-results/
# Optional (with defaults)
export AWS_REGION=us-east-1
export ATHENA_WORKGROUP=primary
export ATHENA_TIMEOUT_SECONDS=60
3. Run
# Start the MCP server (if installed with uv tool install)
aws-athena-mcp
# Or run directly with uv (without installing)
uv tool run aws-athena-mcp
# Or run directly with uvx (without installing)
uvx aws-athena-mcp
# Or run directly with Python
python -m athena_mcp.server
That's it! The server is now running and ready to accept MCP connections.
🤖 Claude Desktop Integration
To use this MCP server with Claude Desktop:
1. Install Claude Desktop
Download and install Claude Desktop if you haven't already.
2. Configure Claude Desktop
Add the following configuration to your claude_desktop_config.json:
Location of config file:
- macOS:
~/Library/Application Support/Claude/claude_desktop_config.json - Windows:
%APPDATA%\Claude\claude_desktop_config.json
Configuration (Option 1 - Using uvx - Recommended):
{
"mcpServers": {
"aws-athena-mcp": {
"command": "uvx",
"args": [
"aws-athena-mcp"
],
"env": {
"ATHENA_S3_OUTPUT_LOCATION": "s3://your-bucket/athena-results/",
"AWS_REGION": "us-east-1",
"ATHENA_WORKGROUP": "primary",
"ATHENA_TIMEOUT_SECONDS": "60"
}
}
}
}
Configuration (Option 2 - Using installed tool):
{
"mcpServers": {
"aws-athena-mcp": {
"command": "aws-athena-mcp",
"env": {
"ATHENA_S3_OUTPUT_LOCATION": "s3://your-bucket/athena-results/",
"AWS_REGION": "us-east-1",
"ATHENA_WORKGROUP": "primary",
"ATHENA_TIMEOUT_SECONDS": "60"
}
}
}
}
Configuration (Option 3 - Using uv tool run):
{
"mcpServers": {
"aws-athena-mcp": {
"command": "uv",
"args": [
"tool",
"run",
"aws-athena-mcp"
],
"env": {
"ATHENA_S3_OUTPUT_LOCATION": "s3://your-bucket/athena-results/",
"AWS_REGION": "us-east-1",
"ATHENA_WORKGROUP": "primary",
"ATHENA_TIMEOUT_SECONDS": "60"
}
}
}
}
Recommended approach: Use Option 1 (uvx) for the most common MCP setup pattern. Option 2 (installed tool) offers better performance as it avoids package resolution on each startup.
3. Set AWS Credentials
Configure your AWS credentials using one of these methods:
# Method 1: Environment variables (add to your shell profile)
export AWS_ACCESS_KEY_ID=your-access-key
export AWS_SECRET_ACCESS_KEY=your-secret-key
# Method 2: AWS CLI
aws configure
# Method 3: AWS Profile
export AWS_PROFILE=your-profile
4. Restart Claude Desktop
Restart Claude Desktop to load the new MCP server configuration.
5. Verify Connection
In Claude Desktop, you should now be able to:
- Execute SQL queries against your Athena databases
- List tables and describe schemas
- Get query results and status
Example conversation:
You: "List all tables in my 'analytics' database"
Claude: I'll help you list the tables in your analytics database using the Athena MCP server.
[Uses list_tables tool]
🛠️ Automated Setup (Alternative)
For easier setup, you can use the included setup script:
# Clone the repository
git clone https://github.com/ColeMurray/aws-athena-mcp
cd aws-athena-mcp
# Run the setup script
python scripts/setup_claude_desktop.py
The script will:
- Check if uv is installed
- Guide you through configuration
- Update your Claude Desktop config file
- Verify AWS credentials
- Provide next steps
You can also copy the example configuration:
cp examples/claude_desktop_config.json ~/Library/Application\ Support/Claude/claude_desktop_config.json
# Then edit the file to add your S3 bucket and AWS settings
🔧 Configuration
The server uses environment variables for configuration:
| Variable | Required | Default | Description |
|---|---|---|---|
ATHENA_S3_OUTPUT_LOCATION |
✅ | - | S3 path for query results |
AWS_REGION |
❌ | us-east-1 |
AWS region |
ATHENA_WORKGROUP |
❌ | None |
Athena workgroup |
ATHENA_TIMEOUT_SECONDS |
❌ | 60 |
Query timeout |
AWS Credentials
Configure AWS credentials using any of these methods:
# Method 1: Environment variables
export AWS_ACCESS_KEY_ID=your-access-key
export AWS_SECRET_ACCESS_KEY=your-secret-key
# Method 2: AWS CLI
aws configure
# Method 3: AWS Profile
export AWS_PROFILE=your-profile
# Method 4: IAM roles (for EC2/Lambda)
# No configuration needed
🔒 Security
Environment Variables
⚠️ NEVER commit credentials to version control!
Use the provided example file to set up your environment:
# Copy the example file
cp examples/environment_variables.example .env
# Edit with your values
nano .env
# Make sure .env is in .gitignore (it already is)
echo ".env" >> .gitignore
AWS Credentials Best Practices
-
Use IAM Roles (recommended for production):
# No credentials needed - uses instance/container role export ATHENA_S3_OUTPUT_LOCATION=s3://your-bucket/results/ -
Use AWS CLI profiles (recommended for development):
aws configure --profile athena-mcp export AWS_PROFILE=athena-mcp -
Use temporary credentials when possible:
aws sts assume-role --role-arn arn:aws:iam::123456789012:role/AthenaRole \ --role-session-name athena-mcp-session -
Avoid long-term access keys in environment variables
Required AWS Permissions
Your AWS credentials need these minimum permissions:
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"athena:StartQueryExecution",
"athena:GetQueryExecution",
"athena:GetQueryResults",
"athena:ListWorkGroups",
"athena:GetWorkGroup"
],
"Resource": "*"
},
{
"Effect": "Allow",
"Action": [
"s3:GetObject",
"s3:PutObject",
"s3:DeleteObject"
],
"Resource": "arn:aws:s3:::your-bucket/athena-results/*"
},
{
"Effect": "Allow",
"Action": [
"s3:ListBucket"
],
"Resource": "arn:aws:s3:::your-bucket"
},
{
"Effect": "Allow",
"Action": [
"glue:GetDatabase",
"glue:GetDatabases",
"glue:GetTable",
"glue:GetTables"
],
"Resource": "*"
}
]
}
SQL Injection Protection
The server includes built-in SQL injection protection:
- Query validation - Dangerous patterns are blocked
- Input sanitization - Database/table names are validated
- Query size limits - Prevents resource exhaustion
- Parameterized queries - When possible
Network Security
For production deployments:
- Use VPC endpoints for AWS services
- Restrict network access to the MCP server
- Use TLS for all communications
- Monitor and log all queries
Monitoring and Auditing
Enable CloudTrail logging for Athena:
{
"eventVersion": "1.05",
"userIdentity": {...},
"eventTime": "2024-01-01T12:00:00Z",
"eventSource": "athena.amazonaws.com",
"eventName": "StartQueryExecution",
"resources": [...]
}
🛠️ Available Tools
The server provides these MCP tools:
Query Execution
run_query- Execute SQL queries against Athenaget_status- Check query execution statusget_result- Get results for completed queries
Schema Discovery
list_tables- List all tables in a databasedescribe_table- Get detailed table schema
📖 Usage Examples
Basic Query Execution
# Using the MCP client (pseudo-code)
result = await mcp_client.call_tool("run_query", {
"database": "default",
"query": "SELECT * FROM my_table LIMIT 10",
"max_rows": 10
})
Schema Discovery
# List tables
tables = await mcp_client.call_tool("list_tables", {
"database": "default"
})
# Describe a table
schema = await mcp_client.call_tool("describe_table", {
"database": "default",
"table_name": "my_table"
})
Handling Timeouts
# Long-running query
result = await mcp_client.call_tool("run_query", {
"database": "default",
"query": "SELECT COUNT(*) FROM large_table"
})
if "query_execution_id" in result:
# Query timed out, check status later
status = await mcp_client.call_tool("get_status", {
"query_execution_id": result["query_execution_id"]
})
🧪 Testing
Test your configuration:
# Test configuration and AWS connection
python scripts/test_connection.py
# Run the test suite
pytest
# Run with coverage
pytest --cov=athena_mcp
🏗️ Development
Setup Development Environment
# Clone and install in development mode
git clone https://github.com/ColeMurray/aws-athena-mcp
cd aws-athena-mcp
pip install -e ".[dev]"
# Run tests
pytest
# Format code
black src tests
isort src tests
# Type checking
mypy src
Project Structure
aws-athena-mcp/
├── src/athena_mcp/ # Main package
│ ├── server.py # MCP server
│ ├── athena.py # AWS Athena client
│ ├── config.py # Configuration
│ └── models.py # Data models
├── src/tools/ # MCP tools
│ ├── query.py # Query tools
│ └── schema.py # Schema tools
├── tests/ # Test suite
├── examples/ # Usage examples
├── scripts/ # Utility scripts
└── docs/ # Documentation
Adding New Tools
- Create tool functions in
src/tools/ - Register them in the appropriate module
- Add tests in
tests/ - Update documentation
Example:
# In src/tools/query.py
def register_query_tools(mcp, athena_client):
@mcp.tool()
async def my_new_tool(param: str) -> str:
"""My new tool description."""
# Implementation here
return result
🔍 Troubleshooting
Common Issues
Configuration Error
❌ Configuration error: ATHENA_S3_OUTPUT_LOCATION environment variable is required
Solution: Set the required environment variable:
export ATHENA_S3_OUTPUT_LOCATION=s3://your-bucket/results/
AWS Credentials Error
❌ AWS credentials error: AWS credentials not found
Solution: Configure AWS credentials (see Configuration section)
Permission Denied
❌ AWS credentials error: AWS credentials are invalid or insufficient permissions
Solution: Ensure your AWS credentials have these permissions:
athena:StartQueryExecutionathena:GetQueryExecutionathena:GetQueryResultsathena:ListWorkGroupss3:GetObject,s3:PutObjecton your S3 bucket
Debug Mode
Enable debug logging:
export PYTHONPATH=src
python -c "
import logging
logging.basicConfig(level=logging.DEBUG)
from athena_mcp.server import main
main()
"
📄 License
MIT License - see LICENSE file for details.
🤝 Contributing
Contributions welcome! Please read our contributing guidelines and:
- Fork the repository
- Create a feature branch
- Make your changes
- Add tests
- Submit a pull request
📞 Support
- Issues: GitHub Issues
- Discussions: GitHub Discussions
- Documentation: docs/
Made with ❤️ for the MCP community
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。