Building Production-Grade AI Data Access: AWS Athena MCP Server

When you're shipping AI features that need real data, the last thing you want is your LLM agents fumbling around with database connections or hallucinating query results. I built an AWS Athena MCP (Model Context Protocol) server that gives AI agents clean, validated access to your data warehouse—no SQL injection risks, no credential leaks, no mysterious timeouts.

The Problem: AI Agents Need Data, Not Demos

Most AI integrations I see in production hit the same wall: data access. Your LLM can write beautiful SQL, but connecting it safely to your actual data warehouse? That's where demos become disasters.

The typical approach—embedding database credentials in prompts or building custom APIs for every query pattern—creates security nightmares and maintenance debt. I needed something cleaner for client projects where AI agents query terabytes of analytics data without breaking things.

What I Built: Clean MCP Interface for AWS Athena

The AWS Athena MCP Server provides five essential tools that AI agents can use safely:

run_query - Execute SQL with built-in injection protection
get_status - Check long-running query progress
get_result - Retrieve completed query results
list_tables - Discover available datasets
describe_table - Get schema information

The key insight: validation happens at the protocol layer, not in your prompts. Query sanitization, timeout handling, and credential management are handled by the MCP server, so your AI agents get clean JSON responses.

Architecture Decisions That Matter

1. FastMCP for Simplicity

I used FastMCP instead of building raw MCP protocol handlers. This cut development time by 60% and gave me async support out of the box.

2. Multi-Layer Security Model

Every SQL query hits a comprehensive validation pipeline:

DANGEROUS_PATTERNS = [
    r";\s*(drop|delete|truncate|alter|create|insert|update)\s+",
    r"--\s*",  # SQL comments
    r"/\*.*?\*/",  # Multi-line comments
    r"xp_cmdshell",  # Command execution
    r"union\s+.*select",  # Union-based injection
    r"information_schema",  # Schema information access
]

Plus identifier sanitization that strips dangerous characters and enforces length limits:

sanitized = re.sub(r"[^a-zA-Z0-9_-]", "", identifier.strip())

3. Timeout Handling for Production

Athena queries can run for hours. The server returns execution IDs for long-running queries instead of blocking:

if await self._wait_for_completion(query_execution_id):
    return query_result
else:
    return query_execution_id  # Check status later

4. Type Safety with Pydantic

All data models use Pydantic for validation and serialization:

class QueryRequest(BaseModel):
    database: str = Field(..., description="The Athena database to query")
    query: str = Field(..., description="SQL query to execute")
    max_rows: int = Field(1000, ge=1, le=10000, description="Maximum rows to return")

Production-Ready Infrastructure

My goal with this server was to build something ready for production. It's built with:

Comprehensive test suite with pytest and async test support
CI/CD pipeline with GitHub Actions running tests across Python 3.10-3.12
Security scanning with CodeQL, Bandit, and Safety checks
Automated setup script for Claude Desktop integration
Type checking with mypy and full type hints
Code quality enforcement with Black and isort

The test coverage includes mocked AWS responses, timeout scenarios, and security validation edge cases.

Real-World Performance

In production deployments, this setup handles:

Query latency: 2-15 seconds for typical analytics queries
Concurrent requests: 20+ simultaneous queries (Athena's default limit)
Data volume: Terabyte-scale datasets without memory issues
Error rate: <0.1% after validation layer implementation
Query size limits: 100KB max to prevent resource exhaustion

Getting Started in 5 Minutes

The server installs via uvx and integrates directly with Claude Desktop:

# Install
uvx install aws-athena-mcp
 
# Configure environment
export ATHENA_S3_OUTPUT_LOCATION=s3://your-bucket/results/
export AWS_REGION=us-east-1
 
# Add to Claude Desktop config
{
  "mcpServers": {
    "aws-athena-mcp": {
      "command": "uvx",
      "args": ["aws-athena-mcp"],
      "env": {
        "ATHENA_S3_OUTPUT_LOCATION": "s3://your-bucket/results/"
      }
    }
  }
}

Your AI agents can now query your data warehouse directly through natural language.

Security Deep Dive

Beyond SQL injection protection, the server implements:

Credential validation on startup with actual AWS API calls
S3 path validation to prevent bucket traversal
Query size limits to prevent DoS attacks
Identifier sanitization for database/table names
Comprehensive logging for audit trails
Error handling that doesn't leak sensitive information

The configuration system validates AWS permissions upfront:

def validate_aws_credentials(self) -> None:
    session = boto3.Session(region_name=self.aws_region)
    client = session.client("athena")
    client.list_work_groups(MaxResults=1)  # Test call

Why This Matters for AI Products

When you're building AI features that need real data—not toy datasets—infrastructure like this becomes critical. The difference between a demo that impresses investors and a feature that ships to customers often comes down to these unglamorous data access patterns.

I've seen too many AI projects stall because teams underestimate the complexity of safe, scalable data integration. Building the plumbing first lets you focus on the AI logic that actually creates value.

What's Next

The full source code includes comprehensive tests, security guidelines, and deployment scripts. The modular architecture makes it straightforward to extend—the same pattern works for any SQL-compatible data source.

For teams shipping AI products with real data requirements, I help design and implement these integration patterns as part of broader AI feature development. The goal is always the same: turn your data into a competitive advantage, not a bottleneck.