Advanced Configuration

Advanced configuration options for performance optimization, cache strategies, and production deployments of the Augments MCP Server.

Advanced Configuration Areas

Deep dive into performance optimization and production-ready configurations

Cache Optimization

TTL strategies & storage

Performance Tuning

Rate limits & connections

Network Configuration

Timeouts & retries

Security Settings

Authentication & tokens

Advanced Cache Configuration

Optimize caching strategies for different deployment scenarios

TTL Strategy Configuration

The server uses intelligent TTL strategies based on content stability and source characteristics.

Setting	Values	Description	Performance Impact
stable	24 hours	Stable releases and production documentation	Low
beta	6 hours	Beta versions and pre-release documentation	Medium
dev	1 hour	Development branches and canary releases	High
default	3 hours	Fallback TTL for unclassified content	Medium

Environment-Specific Cache Settings

Development Environment

Optimized for rapid iteration with short TTL values and local cache storage.

Development Configuration

export AUGMENTS_CACHE_DIR="/tmp/augments-cache-dev"
export AUGMENTS_CACHE_TTL="60"  # 1 minute for rapid testing
export GITHUB_TOKEN="ghp_dev_token"

# Optional: Development-specific overrides
export AUGMENTS_DEV_MODE="true"
export AUGMENTS_LOG_LEVEL="DEBUG"

Cache Storage Optimization

Memory Management

# Cache implementation details
class DocumentationCache:
    def __init__(self, cache_dir: Optional[str] = None):
        self.memory_cache: Dict[str, CacheEntry] = {}
        self.cache_dir = Path(cache_dir or "~/.cache/augments-mcp-server").expanduser()
        
        # Memory cache limits
        self.max_memory_entries = 100
        self.max_entry_size = 5 * 1024 * 1024  # 5MB per entry

Disk Storage Strategy

• Hierarchical storage: memory → disk → source

• Automatic cleanup of expired entries

• Compression for large documentation files

• Atomic writes to prevent corruption

Performance Optimization

Fine-tune performance for high-throughput scenarios

Connection Pool Configuration

HTTP Client Configuration

class GitHubClient:
    def __init__(self, token: Optional[str] = None):
        self.client = httpx.AsyncClient(
            headers=self._get_headers(token),
            timeout=30.0,
            limits=httpx.Limits(
                max_keepalive_connections=5,
                max_connections=10,
                keepalive_expiry=30.0
            )
        )

class WebsiteProvider:
    def __init__(self):
        self.client = httpx.AsyncClient(
            headers={"User-Agent": "Augments-MCP-Server/1.0"},
            timeout=30.0,
            follow_redirects=True
        )

Rate Limiting & Throttling

GitHub API Rate Limiting

# Rate limit tracking and management
async def _check_rate_limit(self) -> None:
    if self.rate_limit_remaining <= 1:
        if datetime.now() < self.rate_limit_reset:
            wait_time = (self.rate_limit_reset - datetime.now()).total_seconds()
            logger.warning("Rate limit exceeded, waiting", wait_seconds=wait_time)
            await asyncio.sleep(wait_time)

# Minimum delay between requests (100ms)
time_since_last = (datetime.now() - self.last_request_time).total_seconds()
if time_since_last < 0.1:
    await asyncio.sleep(0.1 - time_since_last)

Request Batching

• Popular frameworks are auto-cached on server startup

• Background tasks prevent blocking user requests

• Intelligent prefetching based on usage patterns

• Concurrent processing with asyncio task management

Auto-Caching Configuration

Popular Framework Auto-Caching

# Server startup auto-caching
async def _auto_cache_popular_frameworks():
    popular_frameworks = [
        "nextjs", "react", "tailwindcss", 
        "typescript", "shadcn-ui"
    ]
    
    tasks = []
    for framework_name in popular_frameworks:
        config = await registry.get_framework_config(framework_name)
        if config:
            task = asyncio.create_task(
                _cache_framework_documentation(config)
            )
            tasks.append(task)
    
    # Execute all caching tasks concurrently
    await asyncio.gather(*tasks, return_exceptions=True)

Network Configuration

Configure timeouts, retries, and failover strategies

Timeout Configuration

Setting	Values	Description	Performance Impact
HTTP Timeout	30 seconds	Maximum time for HTTP requests	High
Connection Timeout	5 seconds	Time to establish connections	Medium
Keep-Alive Expiry	30 seconds	Connection reuse duration	Low
Read Timeout	30 seconds	Time to read response data	Medium

Failover & Retry Logic

Source Fallback Strategy

async def fetch_documentation(self, config: FrameworkConfig) -> str:
    documentation_parts = []
    
    # Try GitHub source first
    if doc_source.github:
        try:
            github_content = await github_provider.fetch_documentation(...)
            if github_content:
                documentation_parts.append(github_content)
        except Exception as e:
            logger.warning("GitHub source failed, trying fallback", error=str(e))
    
    # Fallback to website source
    if doc_source.website and not documentation_parts:
        try:
            website_content = await website_provider.fetch_content(...)
            if website_content:
                documentation_parts.append(website_content)
        except Exception as e:
            logger.error("All sources failed", error=str(e))
    
    return "\n\n".join(documentation_parts)

Exponential Backoff

• Initial retry delay: 1 second

• Maximum retries: 3 attempts

• Backoff multiplier: 2x per retry

• Circuit breaker for repeated failures

Security & Authentication

Configure secure access to external services and APIs

GitHub Token Management

Token Configuration

# Environment variable setup
export GITHUB_TOKEN="ghp_xxxxxxxxxxxxxxxxxxxx"

# Token permissions required:
# - public_repo (for public repository access)
# - metadata (for repository metadata)

# Rate limits with token:
# - 5000 requests per hour (vs 60 without token)
# - Higher concurrent connection limits

Token Rotation Strategy

• Use different tokens for different environments

• Implement token rotation for long-running deployments

• Monitor token usage and expiration dates

• Fallback to anonymous access if token is invalid

Request Headers & User Agents

HTTP Client Headers

class GitHubClient:
    def _get_headers(self, token: Optional[str]) -> Dict[str, str]:
        headers = {
            "User-Agent": "Augments-MCP-Server/1.0",
            "Accept": "application/vnd.github+json",
            "X-GitHub-Api-Version": "2022-11-28"
        }
        
        if token:
            headers["Authorization"] = f"Bearer {token}"
            
        return headers

class WebsiteProvider:
    def __init__(self):
        self.headers = {
            "User-Agent": "Augments-MCP-Server/1.0 (Documentation Fetcher)",
            "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8"
        }

Monitoring & Observability

Configure logging, metrics, and performance monitoring

Structured Logging

Logging Configuration

import structlog

structlog.configure(
    processors=[
        structlog.dev.ConsoleRenderer()
    ],
    wrapper_class=structlog.make_filtering_bound_logger(20),  # INFO level
    logger_factory=structlog.PrintLoggerFactory(),
    cache_logger_on_first_use=True,
)

logger = structlog.get_logger(__name__)

# Usage examples
logger.info("Framework loaded", framework=framework_name, priority=config.priority)
logger.warning("Rate limit exceeded", remaining=rate_limit_remaining, reset_time=rate_limit_reset)
logger.error("Failed to fetch documentation", framework=framework_name, error=str(e))

Performance Metrics

Cache Statistics

def get_stats(self) -> Dict[str, Any]:
    return {
        "memory_entries": len(self.memory_cache),
        "disk_entries": self._count_disk_entries(),
        "disk_volume_bytes": self._calculate_disk_usage(),
        "cache_directory": str(self.cache_dir),
        "ttl_strategies": self.cache_ttl,
        "hit_rate": self._calculate_hit_rate(),
        "avg_response_time": self._get_avg_response_time()
    }

Registry Statistics

def get_registry_stats(self) -> Dict[str, Any]:
    return {
        "total_frameworks": len(self.frameworks),
        "frameworks_by_category": self._count_by_category(),
        "average_priority": self._calculate_avg_priority(),
        "source_distribution": self._analyze_sources(),
        "last_reload_time": self.last_reload_time.isoformat(),
        "failed_validations": len(self.validation_errors)
    }

Health Check Endpoints

• Framework registry health and loaded framework count

• Cache system status and storage usage

• GitHub API connectivity and rate limit status

• Response time metrics and error rates

• Memory usage and connection pool status