Ultimate MCP Coding Platform

# IMPLEMENTATION CHECKLIST - ULTIMATE MCP PLATFORM ## Systematic Enhancement Plan **Version:** 1.0 **Created:** October 21, 2025 **Priority System:** P0 (Critical) → P1 (High) → P2 (Medium) → P3 (Low) --- ## PRIORITY 0: CRITICAL INFRASTRUCTURE (Complete First) ### ✅ Completion Criteria - [ ] All P0 items implemented and tested - [ ] Load test: Handle 500 RPS for 10 minutes - [ ] Zero database-related failures in stress test - [ ] Performance improvement: >50% latency reduction --- ### P0.1: Add Retry Logic to Neo4jClient ⏱️ 4h | 🎯 High Impact **Location:** `backend/mcp_server/database/neo4j_client.py` **Problem:** Single database failures cause immediate request failures without retry **Implementation:** ```python # Add dependency # requirements.txt: tenacity==8.2.3 from tenacity import ( retry, stop_after_attempt, wait_exponential, retry_if_exception_type, before_sleep_log ) from neo4j.exceptions import ServiceUnavailable, SessionExpired import logging logger = logging.getLogger(__name__) class Neo4jClient: @retry( stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=2, max=10), retry=retry_if_exception_type((ServiceUnavailable, SessionExpired)), before_sleep=before_sleep_log(logger, logging.WARNING) ) async def execute_read_with_retry( self, query: str, parameters: dict[str, Any] | None = None ) -> list[dict[str, Any]]: """Execute read query with automatic retry on transient failures.""" return await self.execute_read(query, parameters) @retry( stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=2, max=10), retry=retry_if_exception_type((ServiceUnavailable, SessionExpired)), before_sleep=before_sleep_log(logger, logging.WARNING) ) async def execute_write_with_retry( self, query: str, parameters: dict[str, Any] | None = None ) -> None: """Execute write query with automatic retry on transient failures.""" return await self.execute_write(query, parameters) ``` **Testing:** ```python # tests/test_neo4j_retry.py async def test_retry_on_service_unavailable(mocker): client = Neo4jClient(...) mock_execute = mocker.patch.object(client, 'execute_read') mock_execute.side_effect = [ ServiceUnavailable("Connection lost"), ServiceUnavailable("Connection lost"), [{"result": "success"}] # Third attempt succeeds ] result = await client.execute_read_with_retry("RETURN 1") assert result == [{"result": "success"}] assert mock_execute.call_count == 3 ``` **Success Metrics:** - [ ] Retry logic handles 99% of transient failures - [ ] Average retry count <1.1 per request - [ ] No cascading failures during network hiccups --- ### P0.2: Integrate Circuit Breakers into Neo4j Operations ⏱️ 6h | 🎯 High Impact **Location:** `backend/mcp_server/database/neo4j_client.py` **Problem:** Cascading failures when database is down **Implementation:** ```python from .utils.circuit_breaker import CircuitBreakerRegistry, CircuitBreakerConfig class Neo4jClient: def __init__(self, uri, user, password, database, **kwargs): # ... existing init ... self.circuit_registry = CircuitBreakerRegistry() self._read_breaker_config = CircuitBreakerConfig( failure_threshold=5, success_threshold=2, timeout_seconds=30.0 ) self._write_breaker_config = CircuitBreakerConfig( failure_threshold=3, # More strict for writes success_threshold=2, timeout_seconds=60.0 ) async def execute_read(self, query: str, parameters: dict[str, Any] | None = None): breaker = await self.circuit_registry.get_or_create( "neo4j_read", self._read_breaker_config ) return await breaker.call(self._execute_read_internal, query, parameters) async def execute_write(self, query: str, parameters: dict[str, Any] | None = None): breaker = await self.circuit_registry.get_or_create( "neo4j_write", self._write_breaker_config ) return await breaker.call(self._execute_write_internal, query, parameters) async def _execute_read_internal(self, query, parameters): """Internal read implementation (original execute_read logic)""" # Move original execute_read logic here ... ``` **Testing:** ```python async def test_circuit_breaker_opens_after_failures(): client = Neo4jClient(...) # Trigger 5 failures for _ in range(5): with pytest.raises(Neo4jError): await client.execute_read("INVALID QUERY") # Circuit should be open now with pytest.raises(CircuitBreakerError): await client.execute_read("RETURN 1") ``` **Success Metrics:** - [ ] Circuit opens after configured threshold - [ ] System fails fast when circuit is open (<10ms) - [ ] Circuit auto-recovers after timeout period --- ### P0.3: Implement ProcessPoolExecutor for Code Execution ⏱️ 4h | 🎯 High Impact **Location:** `backend/mcp_server/tools/exec_tool.py` **Problem:** `asyncio.to_thread` exhausts threadpool under load **Implementation:** ```python import asyncio from concurrent.futures import ProcessPoolExecutor import multiprocessing class ExecutionTool: def __init__(self, neo4j: Neo4jClient) -> None: self._neo4j = neo4j # Use separate process pool for isolation max_workers = min(multiprocessing.cpu_count(), 4) self._executor = ProcessPoolExecutor(max_workers=max_workers) self._semaphore = asyncio.Semaphore(max_workers * 2) async def run(self, request: ExecutionRequest) -> ExecutionResponse: ensure_supported_language(request.language) if request.language != "python": raise ValueError("Execution tool currently supports only Python code.") ensure_safe_python(request.code) # Use semaphore to limit concurrent executions async with self._semaphore: loop = asyncio.get_event_loop() result = await loop.run_in_executor( self._executor, self._execute_python, request ) await self._persist(result) return ExecutionResponse( id=result.id, return_code=result.return_code, stdout=result.stdout, stderr=result.stderr, duration_seconds=result.duration_seconds, ) async def shutdown(self): """Graceful shutdown of executor""" self._executor.shutdown(wait=True) ``` **Testing:** ```python @pytest.mark.asyncio async def test_concurrent_execution_scaling(): tool = ExecutionTool(mock_neo4j) # Execute 100 concurrent requests requests = [ ExecutionRequest(code="print('test')", language="python") for _ in range(100) ] start = time.time() results = await asyncio.gather(*[tool.run(req) for req in requests]) duration = time.time() - start assert all(r.return_code == 0 for r in results) assert duration < 10.0 # Should complete in <10s ``` **Success Metrics:** - [ ] Handle 100 concurrent executions without blocking - [ ] Total time for 100 executions <10s (vs ~25s currently) - [ ] CPU utilization >60% during execution --- ### P0.4: Add Distributed Caching with Redis ⏱️ 8h | 🎯 High Impact **Location:** `backend/mcp_server/utils/cache.py` **Problem:** In-memory cache doesn't scale across instances **Implementation:** ```python import aioredis import json from typing import Any class DistributedCache: """Two-tier cache: L1 (in-memory) + L2 (Redis)""" def __init__( self, redis_url: str = "redis://localhost:6379", local_max_size: int = 500, default_ttl: float = 3600.0, ): self.redis_url = redis_url self.local_cache = InMemoryCache(max_size=local_max_size, default_ttl=default_ttl) self.redis: aioredis.Redis | None = None self.default_ttl = default_ttl async def connect(self): """Initialize Redis connection""" self.redis = await aioredis.from_url( self.redis_url, encoding="utf-8", decode_responses=True ) async def close(self): """Close Redis connection""" if self.redis: await self.redis.close() async def get(self, key: str) -> Any | None: # Try L1 cache first (fastest) value = await self.local_cache.get(key) if value is not None: return value # Try L2 cache (Redis) if self.redis: try: redis_value = await self.redis.get(key) if redis_value: value = json.loads(redis_value) # Populate L1 cache await self.local_cache.set(key, value) return value except Exception as e: logger.error(f"Redis get error: {e}") return None async def set(self, key: str, value: Any, ttl: float | None = None) -> None: effective_ttl = ttl or self.default_ttl # Set in L1 (local) await self.local_cache.set(key, value, effective_ttl) # Set in L2 (Redis) if self.redis: try: await self.redis.setex( key, int(effective_ttl), json.dumps(value, default=str) ) except Exception as e: logger.error(f"Redis set error: {e}") async def delete(self, key: str) -> bool: # Delete from both tiers local_deleted = await self.local_cache.delete(key) if self.redis: try: await self.redis.delete(key) except Exception as e: logger.error(f"Redis delete error: {e}") return local_deleted ``` **Configuration:** ```python # backend/mcp_server/config.py class CacheConfig(SettingsBase): enabled: bool = Field(default=True) backend: str = Field(default="redis") # "memory" or "redis" redis_url: str = Field(default="redis://localhost:6379") local_max_size: int = Field(default=500) default_ttl: int = Field(default=3600) ``` **Testing:** ```python async def test_distributed_cache_l1_l2(): cache = DistributedCache(redis_url="redis://localhost:6379") await cache.connect() # Set value await cache.set("test_key", {"data": "value"}) # Clear L1 to force L2 lookup await cache.local_cache.clear() # Should retrieve from L2 (Redis) value = await cache.get("test_key") assert value == {"data": "value"} await cache.close() ``` **Success Metrics:** - [ ] Cache hit rate >80% with multi-instance deployment - [ ] L1 hit latency <1ms, L2 hit latency <10ms - [ ] Zero cache inconsistencies across instances --- ### P0.5: Optimize Neo4j Connection Pool ⏱️ 2h | 🎯 Medium Impact **Location:** `backend/mcp_server/database/neo4j_client.py` **Problem:** Default pool sizing not optimized for workload **Implementation:** ```python class Neo4jClient: def __init__( self, uri: str, user: str, password: str, database: str, *, max_connection_pool_size: int | None = None, connection_acquisition_timeout: float | None = None, max_connection_lifetime: int = 3600, # 1 hour (was 300s) ) -> None: # Auto-calculate optimal pool size if not provided if max_connection_pool_size is None: import multiprocessing cpu_count = multiprocessing.cpu_count() # Formula: pool_size = cpu_count * 2 + spare max_connection_pool_size = min(cpu_count * 2 + 4, 100) if connection_acquisition_timeout is None: # Fail fast - don't queue indefinitely connection_acquisition_timeout = 5.0 self._driver = AsyncGraphDatabase.driver( uri, auth=(user, password), max_connection_lifetime=max_connection_lifetime, max_connection_pool_size=max_connection_pool_size, connection_acquisition_timeout=connection_acquisition_timeout, # Additional optimizations keep_alive=True, connection_timeout=10.0, max_transaction_retry_time=15.0, ) self._database = database logger.info( "Neo4j connection pool configured", extra={ "max_pool_size": max_connection_pool_size, "acquisition_timeout": connection_acquisition_timeout, "max_lifetime": max_connection_lifetime, } ) ``` **Configuration:** ```python # config/production.yaml database: max_connection_pool_size: 50 # Tune based on load connection_acquisition_timeout: 5.0 max_connection_lifetime: 3600 ``` **Success Metrics:** - [ ] Connection acquisition time <10ms at P99 - [ ] Pool utilization 50-70% under normal load - [ ] Zero connection timeout errors under 500 RPS --- ### P0.6: Add Query Result Caching ⏱️ 4h | 🎯 High Impact **Location:** `backend/mcp_server/database/neo4j_client.py` **Problem:** Expensive metrics queries re-executed every request **Implementation:** ```python from functools import wraps import hashlib class Neo4jClient: def __init__(self, ...): # ... existing init ... self.query_cache = DistributedCache() # Or InMemoryCache if Redis not available def cached_query(self, ttl: float = 60.0): """Decorator to cache query results""" def decorator(func): @wraps(func) async def wrapper(*args, **kwargs): # Generate cache key from query + params cache_key = self._generate_cache_key(func.__name__, args, kwargs) # Try cache first cached_result = await self.query_cache.get(cache_key) if cached_result is not None: logger.debug(f"Query cache hit: {func.__name__}") return cached_result # Execute query result = await func(*args, **kwargs) # Cache result await self.query_cache.set(cache_key, result, ttl) return result return wrapper return decorator def _generate_cache_key(self, func_name: str, args, kwargs) -> str: """Generate deterministic cache key""" import json key_data = { "func": func_name, "args": args[1:], # Skip self "kwargs": kwargs } key_str = json.dumps(key_data, sort_keys=True, default=str) return f"neo4j:{hashlib.sha256(key_str.encode()).hexdigest()[:16]}" @cached_query(ttl=60.0) # Cache for 60 seconds async def get_metrics(self) -> GraphMetrics: """Get graph metrics with caching""" # ... existing implementation ... @cached_query(ttl=300.0) # Cache for 5 minutes async def execute_read(self, query: str, parameters: dict[str, Any] | None = None): """Execute read query with caching for safe queries""" # Only cache if query is deterministic and safe if self._is_cacheable_query(query): return await self._execute_read_cached(query, parameters) return await self._execute_read_internal(query, parameters) def _is_cacheable_query(self, query: str) -> bool: """Check if query is safe to cache""" query_upper = query.upper().strip() # Only cache pure MATCH queries without time-based functions return ( query_upper.startswith("MATCH") and "CALL" not in query_upper and "DATETIME()" not in query_upper and "TIMESTAMP()" not in query_upper ) ``` **Testing:** ```python async def test_query_result_caching(): client = Neo4jClient(...) # First call - cache miss result1 = await client.get_metrics() # Second call - cache hit start = time.time() result2 = await client.get_metrics() duration = time.time() - start assert result1 == result2 assert duration < 0.01 # <10ms from cache ``` **Success Metrics:** - [ ] Metrics endpoint latency reduced by 90% (100ms → 10ms) - [ ] Cache hit rate >70% for read queries - [ ] No stale data (cache invalidation works correctly) --- ## PRIORITY 1: SECURITY & PERFORMANCE ENHANCEMENTS ### P1.1: Implement Comprehensive Input Sanitization ⏱️ 6h **Location:** Create `backend/mcp_server/utils/sanitization.py` **Implementation:** ```python import bleach import html from typing import Any, Dict class InputSanitizer: """Comprehensive input sanitization for all user inputs""" @staticmethod def sanitize_string(value: str, max_length: int = 10000) -> str: """Sanitize string input - remove dangerous characters""" if len(value) > max_length: raise ValueError(f"String exceeds max length of {max_length}") # Remove null bytes and control characters (except newlines/tabs) sanitized = re.sub(r'[\x00-\x08\x0B\x0C\x0E-\x1F\x7F]', '', value) # HTML escape to prevent XSS sanitized = html.escape(sanitized) return sanitized @staticmethod def sanitize_dict(data: Dict[str, Any], max_depth: int = 5) -> Dict[str, Any]: """Recursively sanitize dictionary""" if max_depth <= 0: raise ValueError("Maximum nesting depth exceeded") sanitized = {} for key, value in data.items(): # Sanitize key clean_key = InputSanitizer.sanitize_string(str(key), max_length=256) # Sanitize value based on type if isinstance(value, str): clean_value = InputSanitizer.sanitize_string(value) elif isinstance(value, dict): clean_value = InputSanitizer.sanitize_dict(value, max_depth - 1) elif isinstance(value, list): clean_value = [ InputSanitizer.sanitize_string(item) if isinstance(item, str) else item for item in value ] else: clean_value = value sanitized[clean_key] = clean_value return sanitized ``` **Integration:** ```python # In tools/graph_tool.py async def _upsert_node(self, node: GraphNode) -> None: ensure_valid_identifier(node.key, field="node.key") # Sanitize properties before storage sanitized_props = InputSanitizer.sanitize_dict(node.properties) labels = ["GraphNode"] + [self._normalise_label(label) for label in node.labels] label_fragment = ":".join(labels) await self._neo4j.execute_write( f"MERGE (n:{label_fragment} {{key: $key}}) SET n += $props", {"key": node.key, "props": sanitized_props}, ) ``` --- ### P1.2: Add User-Based Rate Limiting ⏱️ 4h **Implementation:** ```python from slowapi import Limiter from slowapi.util import get_remote_address def get_user_identifier(request: Request) -> str: """Extract user ID from JWT token for rate limiting""" auth = request.headers.get("Authorization") if auth and auth.startswith("Bearer "): token = auth[7:] try: payload = jwt.decode(token, settings.auth_token, algorithms=["HS256"]) return payload.get("sub", get_remote_address(request)) except: pass return get_remote_address(request) # Update limiter limiter = Limiter(key_func=get_user_identifier) ``` --- ### P1.3: Enhance JWT Secret Validation ⏱️ 2h **Implementation:** ```python import secrets def validate_secret_entropy(secret: str, min_bits: int = 256): """Ensure secret has sufficient entropy""" if len(secret) < min_bits // 8: raise ValueError( f"Secret must be at least {min_bits // 8} characters " f"for {min_bits}-bit security" ) # Check for common weak patterns weak_patterns = ["password", "secret", "test", "demo", "change-me"] if any(pattern in secret.lower() for pattern in weak_patterns): raise ValueError("Secret contains weak patterns") # In config.py class Settings: def __init__(self): # ... existing code ... validate_secret_entropy(self.auth_token, min_bits=256) ``` --- ### P1.4: Implement Batch Graph Operations ⏱️ 6h **Location:** `backend/mcp_server/tools/graph_tool.py` **Implementation:** ```python async def upsert(self, payload: GraphUpsertPayload) -> GraphUpsertResponse: # Use single transaction for all operations async def batch_upsert(tx): # Batch insert nodes for node in payload.nodes: ensure_valid_identifier(node.key, field="node.key") labels = ["GraphNode"] + [self._normalise_label(l) for l in node.labels] label_fragment = ":".join(labels) await tx.run( f"MERGE (n:{label_fragment} {{key: $key}}) SET n += $props", {"key": node.key, "props": node.properties} ) # Batch insert relationships for rel in payload.relationships: ensure_valid_identifier(rel.start, field="relationship.start") ensure_valid_identifier(rel.end, field="relationship.end") rel_type = self._normalise_label(rel.type) await tx.run( f"MATCH (start:GraphNode {{key: $start}}) " f"MATCH (end:GraphNode {{key: $end}}) " f"MERGE (start)-[r:{rel_type}]->(end) " "SET r += $props", {"start": rel.start, "end": rel.end, "props": rel.properties} ) await self._neo4j.execute_write_transaction(batch_upsert) metrics = await self._neo4j.get_metrics() return GraphUpsertResponse(metrics=metrics) ``` --- ### P1.5: Add Request Correlation IDs ⏱️ 4h **Implementation:** ```python class CorrelationMiddleware(BaseHTTPMiddleware): """Propagate correlation IDs across service boundaries""" async def dispatch(self, request: Request, call_next): correlation_id = request.headers.get("X-Correlation-ID", str(uuid.uuid4())) request.state.correlation_id = correlation_id # Set in logging context structlog.contextvars.bind_contextvars(correlation_id=correlation_id) response = await call_next(request) response.headers["X-Correlation-ID"] = correlation_id structlog.contextvars.unbind_contextvars("correlation_id") return response # Add to app app.add_middleware(CorrelationMiddleware) ``` --- ## PRIORITY 2: CODE QUALITY IMPROVEMENTS ### P2.1: Add Comprehensive Error Context ⏱️ 4h **Pattern:** ```python # Before raise ValueError("Token contains no valid roles") # After raise ValueError( f"Token contains no valid roles. " f"Found role strings: {role_strings}, " f"Expected one of: {[r.value for r in Role]}" ) ``` ### P2.2: Standardize Logging Patterns ⏱️ 4h **Pattern:** ```python # Use structlog everywhere import structlog logger = structlog.get_logger(__name__) logger.info( "query_executed", query_type="read", duration_ms=duration * 1000, correlation_id=request.state.correlation_id ) ``` ### P2.3: Add API Versioning ⏱️ 4h **Implementation:** ```python from fastapi import APIRouter v1_router = APIRouter(prefix="/api/v1") @v1_router.post("/execute_code") async def execute_code_v1(...): ... app.include_router(v1_router) ``` --- ## TESTING CHECKLIST ### Load Tests - [ ] 500 RPS sustained for 10 minutes - [ ] 1000 concurrent users - [ ] Memory usage stable under load - [ ] No connection pool exhaustion ### Chaos Tests - [ ] Database network partition recovery - [ ] Redis failure graceful degradation - [ ] Process crash recovery ### Security Tests - [ ] Cypher injection attempts blocked - [ ] JWT forgery attempts fail - [ ] Rate limit bypasses prevented - [ ] XSS payloads sanitized --- ## DEPLOYMENT CHECKLIST ### Pre-Deployment - [ ] All tests passing (unit + integration + load) - [ ] Code coverage >90% - [ ] Security scan passed - [ ] Performance benchmarks met ### Deployment - [ ] Database migrations applied - [ ] Redis cluster configured - [ ] Monitoring dashboards created - [ ] Alerts configured ### Post-Deployment - [ ] Monitor error rates <0.1% for 24h - [ ] Validate latency P99 <200ms - [ ] Check resource utilization <70% - [ ] Verify logs for correlation IDs --- ## ROLLBACK PLAN If any issues occur: 1. **Immediate:** Revert to previous version 2. **Within 1h:** Identify root cause 3. **Within 4h:** Fix or escalate 4. **Within 24h:** Post-mortem document **Rollback Command:** ```bash git revert HEAD~1 docker-compose up -d --build ``` --- **Document Version:** 1.0 **Last Updated:** October 21, 2025