# Semantic Scholar MCP Server Architecture

## Table of Contents

1. [Overview](#overview)
2. [System Architecture](#system-architecture)
3. [Design Principles](#design-principles)
4. [Component Architecture](#component-architecture)
5. [Data Flow](#data-flow)
6. [Design Decisions](#design-decisions)
7. [Performance Considerations](#performance-considerations)
8. [Security Model](#security-model)
9. [Scalability Strategies](#scalability-strategies)
10. [Technology Stack](#technology-stack)

## Overview

The Semantic Scholar MCP Server is designed as a bridge between AI assistants (like Claude) and the Semantic Scholar academic database. It follows a layered architecture pattern with clear separation of concerns, enabling maintainability, testability, and extensibility.

### Key Architectural Goals

- **Modularity**: Components are loosely coupled and highly cohesive
- **Performance**: Async/await pattern for non-blocking I/O operations
- **Reliability**: Comprehensive error handling and retry mechanisms
- **Extensibility**: Clean abstractions for easy feature additions
- **Observability**: Built-in metrics and structured logging

## System Architecture

```
┌─────────────────────────────────────────────────────────────────────┐
│                            Client Layer                              │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐  ┌────────────┐ │
│  │Claude/LLM   │  │   VS Code   │  │   CLI Tool  │  │Custom Apps │ │
│  └──────┬──────┘  └──────┬──────┘  └──────┬──────┘  └─────┬──────┘ │
└─────────┼────────────────┼────────────────┼───────────────┼────────┘
          │                │                │               │
          └────────────────┴────────────────┴───────────────┘
                                   │
                          MCP Protocol (JSON-RPC)
                                   │
┌─────────────────────────────────┴─────────────────────────────────┐
│                         MCP Server Layer                           │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐           │
│  │Tool Handlers │  │Resource      │  │Prompt        │           │
│  │              │  │Providers     │  │Templates     │           │
│  └──────┬───────┘  └──────┬───────┘  └──────┬───────┘           │
│         └─────────────────┴─────────────────┘                    │
│                           │                                       │
│  ┌────────────────────────┴────────────────────────┐            │
│  │              Request Router & Validator         │            │
│  └────────────────────────┬────────────────────────┘            │
└───────────────────────────┼──────────────────────────────────┘
                           │
┌───────────────────────────┴──────────────────────────────────┐
│                    Business Logic Layer                       │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐         │
│  │Paper Service│  │Author       │  │Citation     │         │
│  │             │  │Service      │  │Service      │         │
│  └──────┬──────┘  └──────┬──────┘  └──────┬──────┘         │
│         └─────────────────┴─────────────────┘               │
│                           │                                  │
│  ┌────────────────────────┴────────────────────┐           │
│  │           Domain Model Repository           │           │
│  └────────────────────────┬────────────────────┘           │
└───────────────────────────┼─────────────────────────────┘
                           │
┌───────────────────────────┴─────────────────────────────┐
│                   Data Access Layer                      │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐    │
│  │API Client   │  │Cache Manager│  │Rate Limiter │    │
│  │(Async)      │  │(LRU/TTL)    │  │(Token Bucket)│    │
│  └──────┬──────┘  └──────┬──────┘  └──────┬──────┘    │
│         └─────────────────┴─────────────────┘          │
└─────────────────────────┼───────────────────────────┘
                         │
                    External API
                         │
              ┌──────────┴──────────┐
              │ Semantic Scholar API│
              └────────────────────┘
```

## Design Principles

### 1. Domain-Driven Design (DDD)

The architecture follows DDD principles with clear domain boundaries:

- **Core Domain**: Academic papers, authors, citations
- **Supporting Domains**: Search, recommendations, metrics
- **Generic Subdomains**: Caching, rate limiting, logging

### 2. SOLID Principles

- **Single Responsibility**: Each class has one reason to change
- **Open/Closed**: Open for extension, closed for modification
- **Liskov Substitution**: Derived classes are substitutable
- **Interface Segregation**: Many specific interfaces over general ones
- **Dependency Inversion**: Depend on abstractions, not concretions

### 3. Clean Architecture

- **Independence**: Business logic is independent of frameworks
- **Testability**: Business rules can be tested without external elements
- **UI Independence**: The system can work with any UI
- **Database Independence**: Business rules don't know about the database
- **External Agency Independence**: Business rules don't know about the outside world

### 4. Async-First Design

All I/O operations are asynchronous to maximize throughput:

```python
async def search_papers(self, query: str) -> SearchResult:
    """Async paper search with concurrent operations."""
    async with self._client as client:
        # Concurrent cache check and API call
        cache_task = self._cache.get(cache_key)
        api_task = client.search(query)
        
        cached, result = await asyncio.gather(
            cache_task, api_task, return_exceptions=True
        )
```

## Component Architecture

### 1. MCP Server Layer

**Responsibilities:**
- Handle MCP protocol communication
- Route requests to appropriate handlers
- Validate input/output according to MCP spec
- Manage tool, resource, and prompt definitions

**Key Components:**
```python
class MCPServer:
    def __init__(self):
        self.tools = ToolRegistry()
        self.resources = ResourceProvider()
        self.prompts = PromptManager()
        
    async def handle_request(self, request: MCPRequest) -> MCPResponse:
        """Main request handler with routing logic."""
```

### 2. Business Logic Layer

**Responsibilities:**
- Implement core business rules
- Orchestrate complex operations
- Transform between domain models and DTOs
- Enforce business invariants

**Key Services:**

```python
class PaperService:
    """Handles paper-related operations."""
    
    async def search(
        self,
        query: SearchQuery,
        pagination: PaginationParams
    ) -> PagedResult[Paper]:
        """Search papers with business logic validation."""
        
class AuthorService:
    """Handles author-related operations."""
    
    async def get_author_metrics(
        self,
        author_id: str
    ) -> AuthorMetrics:
        """Calculate author metrics including h-index."""
```

### 3. Data Access Layer

**Responsibilities:**
- Abstract external API interactions
- Implement caching strategies
- Handle rate limiting
- Manage connection pooling

**Key Components:**

```python
class SemanticScholarClient:
    """Async HTTP client with retry logic."""
    
    def __init__(self):
        self.session = httpx.AsyncClient(
            limits=httpx.Limits(
                max_connections=100,
                max_keepalive_connections=20
            )
        )
        
class CacheManager:
    """Multi-level cache implementation."""
    
    def __init__(self):
        self.memory_cache = LRUCache(maxsize=1000)
        self.disk_cache = DiskCache(max_size_gb=1)
```

### 4. Cross-Cutting Concerns

**Logging:**
```python
class StructuredLogger:
    """Structured logging with context propagation."""
    
    async def log_with_context(
        self,
        level: LogLevel,
        message: str,
        **context
    ):
        """Log with request context and correlation ID."""
```

**Metrics:**
```python
class MetricsCollector:
    """Collect and export metrics."""
    
    def record_api_call(
        self,
        endpoint: str,
        duration: float,
        status: int
    ):
        """Record API call metrics."""
```

**Error Handling:**
```python
class ErrorHandler:
    """Centralized error handling with recovery strategies."""
    
    async def handle_with_fallback(
        self,
        operation: Callable,
        fallback: Callable
    ):
        """Execute with fallback on failure."""
```

## Data Flow

### 1. Search Request Flow

```
1. Client sends search_papers request via MCP
2. MCPServer validates request format
3. ToolHandler extracts parameters
4. PaperService applies business rules
5. CacheManager checks for cached results
6. If not cached:
   a. RateLimiter checks quota
   b. APIClient makes HTTP request
   c. RetryStrategy handles transient failures
7. ResponseTransformer converts to domain model
8. CacheManager stores result
9. MCPServer formats response
10. Client receives results
```

### 2. Caching Strategy

```
┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│   Request   │────▶│Memory Cache │────▶│ Disk Cache  │
└─────────────┘     └─────┬───────┘     └─────┬───────┘
                          │ miss              │ miss
                          ▼                   ▼
                    ┌─────────────┐     ┌─────────────┐
                    │   Return    │◀────│External API │
                    └─────────────┘     └─────────────┘
```

**Cache Key Generation:**
```python
def generate_cache_key(
    operation: str,
    params: Dict[str, Any]
) -> str:
    """Generate deterministic cache key."""
    normalized = json.dumps(params, sort_keys=True)
    return f"{operation}:{hashlib.sha256(normalized.encode()).hexdigest()}"
```

## Design Decisions

### 1. Why MCP?

**Rationale:**
- Standardized protocol for AI assistant integration
- Language-agnostic communication
- Built-in support for tools, resources, and prompts
- Extensible for future capabilities

**Trade-offs:**
- Additional protocol overhead
- Limited to MCP-compatible clients

### 2. Why Async/Await?

**Rationale:**
- Non-blocking I/O for better concurrency
- Natural fit for API-heavy workloads
- Better resource utilization
- Simplified concurrent code

**Trade-offs:**
- Increased complexity for developers
- Requires async-compatible libraries

### 3. Why Pydantic?

**Rationale:**
- Runtime type validation
- Automatic serialization/deserialization
- Self-documenting models
- Integration with FastAPI/modern frameworks

**Trade-offs:**
- Performance overhead for validation
- Learning curve for advanced features

### 4. Multi-Level Caching

**Rationale:**
- Reduce API calls and costs
- Improve response times
- Handle rate limits gracefully
- Offline capability

**Implementation:**
```python
class MultiLevelCache:
    """Hierarchical cache with fallback."""
    
    def __init__(self):
        self.l1_cache = MemoryCache(ttl=300)  # 5 min
        self.l2_cache = DiskCache(ttl=3600)   # 1 hour
        self.l3_cache = RedisCache(ttl=86400) # 1 day
```

### 5. Repository Pattern

**Rationale:**
- Abstract data access logic
- Enable testing with mocks
- Support multiple data sources
- Consistent query interface

**Example:**
```python
class PaperRepository:
    """Abstract repository for papers."""
    
    async def find_by_id(self, paper_id: str) -> Optional[Paper]:
        """Find paper by ID from any source."""
        
    async def search(
        self,
        query: SearchQuery,
        pagination: PaginationParams
    ) -> PagedResult[Paper]:
        """Search papers with pagination."""
```

## Performance Considerations

### 1. Connection Pooling

Maintain persistent connections to reduce latency:

```python
httpx.AsyncClient(
    limits=httpx.Limits(
        max_connections=100,
        max_keepalive_connections=20,
        keepalive_expiry=30
    )
)
```

### 2. Request Batching

Batch multiple requests when possible:

```python
async def get_papers_batch(
    self,
    paper_ids: List[str]
) -> List[Paper]:
    """Fetch multiple papers in one request."""
    # Group into batches of 50
    batches = [paper_ids[i:i+50] for i in range(0, len(paper_ids), 50)]
    results = await asyncio.gather(*[
        self._fetch_batch(batch) for batch in batches
    ])
    return list(itertools.chain.from_iterable(results))
```

### 3. Field Selection

Only request necessary fields to reduce payload size:

```python
MINIMAL_FIELDS = ["paperId", "title", "year", "authors"]
STANDARD_FIELDS = MINIMAL_FIELDS + ["abstract", "citationCount"]
DETAILED_FIELDS = STANDARD_FIELDS + ["references", "citations"]
```

### 4. Lazy Loading

Load expensive data only when needed:

```python
class Paper:
    def __init__(self, data: dict):
        self._data = data
        self._citations = None
        
    @property
    async def citations(self) -> List[Citation]:
        """Lazy load citations."""
        if self._citations is None:
            self._citations = await self._load_citations()
        return self._citations
```

### 5. Circuit Breaker

Prevent cascading failures:

```python
class CircuitBreaker:
    """Implement circuit breaker pattern."""
    
    def __init__(
        self,
        failure_threshold: int = 5,
        recovery_timeout: int = 60
    ):
        self.failure_count = 0
        self.last_failure_time = None
        self.state = CircuitState.CLOSED
```

## Security Model

### 1. API Key Management

**Storage:**
- Environment variables for configuration
- Secure key vault for production
- Never commit keys to version control

**Rotation:**
```python
class APIKeyManager:
    """Manage API key rotation."""
    
    async def rotate_key(self):
        """Rotate API key with zero downtime."""
        new_key = await self._provision_new_key()
        await self._update_clients(new_key)
        await self._revoke_old_key()
```

### 2. Input Validation

**Sanitization:**
```python
def sanitize_query(query: str) -> str:
    """Sanitize search query."""
    # Remove special characters
    query = re.sub(r'[^\w\s-]', '', query)
    # Limit length
    return query[:500]
```

**Validation:**
```python
class SearchQueryValidator:
    """Validate search parameters."""
    
    def validate(self, query: SearchQuery) -> None:
        if not query.query or len(query.query.strip()) == 0:
            raise ValidationError("Query cannot be empty")
        if query.limit > 100:
            raise ValidationError("Limit cannot exceed 100")
```

### 3. Rate Limiting

**Implementation:**
```python
class TokenBucketRateLimiter:
    """Token bucket rate limiting."""
    
    def __init__(self, rate: int, capacity: int):
        self.rate = rate
        self.capacity = capacity
        self.tokens = capacity
        self.last_update = time.time()
        
    async def acquire(self, tokens: int = 1) -> bool:
        """Acquire tokens for request."""
        self._refill()
        if self.tokens >= tokens:
            self.tokens -= tokens
            return True
        return False
```

### 4. Data Privacy

**PII Handling:**
- No storage of personally identifiable information
- Anonymize user queries in logs
- Comply with GDPR/CCPA requirements

**Audit Logging:**
```python
class AuditLogger:
    """Log security-relevant events."""
    
    async def log_access(
        self,
        user_id: str,
        resource: str,
        action: str,
        result: str
    ):
        """Log resource access for audit trail."""
```

## Scalability Strategies

### 1. Horizontal Scaling

**Load Balancing:**
```
┌─────────────┐
│Load Balancer│
└──────┬──────┘
       │
┌──────┴──────┬──────────┬──────────┐
│             │          │          │
▼             ▼          ▼          ▼
Server 1    Server 2   Server 3   Server N
```

**Session Affinity:**
- Not required (stateless design)
- Each request is independent

### 2. Caching Layers

**Distributed Cache:**
```python
class RedisCache:
    """Distributed cache for horizontal scaling."""
    
    def __init__(self, redis_url: str):
        self.redis = aioredis.from_url(redis_url)
        
    async def get(self, key: str) -> Optional[Any]:
        """Get from distributed cache."""
        value = await self.redis.get(key)
        return pickle.loads(value) if value else None
```

### 3. Database Sharding

**Strategy for future expansion:**
```python
class ShardedRepository:
    """Repository with sharding support."""
    
    def get_shard(self, key: str) -> int:
        """Determine shard for key."""
        return hash(key) % self.num_shards
```

### 4. Async Worker Pools

**Background Processing:**
```python
class WorkerPool:
    """Async worker pool for CPU-bound tasks."""
    
    def __init__(self, num_workers: int = 4):
        self.executor = ProcessPoolExecutor(max_workers=num_workers)
        
    async def process(self, func: Callable, *args):
        """Process in worker pool."""
        loop = asyncio.get_event_loop()
        return await loop.run_in_executor(self.executor, func, *args)
```

### 5. Event-Driven Architecture

**For future microservices:**
```python
class EventBus:
    """Event bus for decoupled communication."""
    
    async def publish(self, event: Event):
        """Publish event to subscribers."""
        
    async def subscribe(self, event_type: str, handler: Callable):
        """Subscribe to event type."""
```

## Technology Stack

### Core Technologies

1. **Python 3.9+**
   - Modern async/await support
   - Type hints and annotations
   - Performance improvements

2. **MCP SDK**
   - Model Context Protocol implementation
   - Standardized AI assistant integration

3. **httpx**
   - Async HTTP client
   - HTTP/2 support
   - Connection pooling

4. **Pydantic**
   - Data validation
   - Serialization/deserialization
   - Type safety

5. **tenacity**
   - Retry logic
   - Exponential backoff
   - Circuit breaker patterns

### Development Tools

1. **pytest**
   - Unit and integration testing
   - Async test support
   - Fixtures and parametrization

2. **mypy**
   - Static type checking
   - Type inference
   - Error prevention

3. **black/ruff**
   - Code formatting
   - Linting
   - Style enforcement

4. **pre-commit**
   - Git hooks
   - Automated checks
   - Code quality

### Infrastructure

1. **Docker**
   - Containerization
   - Consistent environments
   - Easy deployment

2. **Redis** (optional)
   - Distributed caching
   - Pub/sub for events
   - Session storage

3. **Prometheus/Grafana** (optional)
   - Metrics collection
   - Monitoring dashboards
   - Alerting

### API Integration

1. **Semantic Scholar API**
   - RESTful endpoints
   - Graph API for relationships
   - Batch operations

2. **Rate Limiting**
   - Token bucket algorithm
   - Adaptive throttling
   - Backoff strategies

## Conclusion

The Semantic Scholar MCP Server architecture is designed to be:

- **Scalable**: Can handle increased load through horizontal scaling
- **Maintainable**: Clear separation of concerns and modular design
- **Performant**: Async operations and intelligent caching
- **Reliable**: Comprehensive error handling and retry mechanisms
- **Secure**: Input validation and rate limiting
- **Extensible**: Easy to add new features and integrations

The architecture follows industry best practices while remaining pragmatic and focused on delivering value to end users.