Thị trường AI API đang bùng nổ với mức giá cạnh tranh khốc liệt năm 2026. Trước khi đi sâu vào giải pháp kỹ thuật, hãy cùng xem bức tranh chi phí thực tế mà doanh nghiệp đang đối mặt:
| Model | Giá Output/MTok | 10M Token/Tháng | Độ trễ trung bình |
|---|---|---|---|
| GPT-4.1 | $8.00 | $80 | ~800ms |
| Claude Sonnet 4.5 | $15.00 | $150 | ~650ms |
| Gemini 2.5 Flash | $2.50 | $25 | ~300ms |
| DeepSeek V3.2 | $0.42 | $4.20 | ~450ms |
| HolySheep AI | Tương đương DeepSeek V3.2 | $4.20 | <50ms |
Từ bảng so sánh trên, có thể thấy DeepSeek V3.2 qua HolySheep AI tiết kiệm đến 97% chi phí so với Claude Sonnet 4.5, kèm độ trễ chỉ 50ms thay vì 650ms. Đặc biệt với doanh nghiệp cần triển khai MCP protocol trong hạ tầng nội bộ, việc lựa chọn gateway có khả năng audit log và bảo mật trở nên then chốt.
MCP Protocol Là Gì và Tại Sao Cần Security Gateway?
Model Context Protocol (MCP) là giao thức chuẩn hóa kết nối giữa AI agent và data source. Trong môi trường enterprise, MCP thường chứa:
- Dữ liệu khách hàng nhạy cảm (PII, financial records)
- Internal API keys và credentials
- Business logic và proprietary algorithms
- Real-time streaming data từ IoT devices
Không có security gateway, traffic MCP đi trực tiếp ra external API, tạo ra nhiều rủi ro nghiêm trọng:
- Data leakage qua prompt injection
- Unauthorized access đến internal resources
- Compliance violation (GDPR, SOC2, ISO 27001)
- No visibility vào request/response patterns
- Không có audit trail cho forensic analysis
Kiến Trúc Security Gateway Cho MCP
1. Reverse Proxy với TLS Termination
Security gateway đầu tiên cần đảm bảo all traffic được mã hóa end-to-end. Kiến trúc khuyến nghị:
/etc/nginx/conf.d/mcp-gateway.conf
server {
listen 8443 ssl http2;
server_name mcp-gateway.internal.corp;
# SSL Termination
ssl_certificate /etc/ssl/certs/internal-wildcard.crt;
ssl_certificate_key /etc/ssl/private/internal-wildcard.key;
ssl_protocols TLSv1.3;
ssl_ciphers ECDHE-ECDSA-AES256-GCM-SHA384:ECDHE-RSA-AES256-GCM-SHA384;
ssl_prefer_server_ciphers on;
# MCP Protocol Upgrade Handling
location /mcp/v1/ {
proxy_pass https://mcp-backend.internal:8080;
proxy_http_version 1.1;
proxy_set_header Upgrade $http_upgrade;
proxy_set_header Connection "upgrade";
proxy_set_header Host $host;
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
proxy_set_header X-Forwarded-Proto $scheme;
# Timeout settings for long-running MCP sessions
proxy_read_timeout 300s;
proxy_send_timeout 300s;
# Buffering for audit purposes
proxy_buffering on;
proxy_buffer_size 128k;
proxy_buffers 4 256k;
}
# Rate limiting per client
limit_req zone=mcp_limit burst=100 nodelay;
limit_conn mcp_conn 50;
}
limit_req_zone $binary_remote_addr zone=mcp_limit:10m rate=10r/s;
limit_conn_zone $binary_remote_addr zone=mcp_conn:10m;
2. Authentication và Authorization Layer
Triển khai JWT-based auth với RBAC cho MCP resources:
mcp_security/auth.py
from fastapi import FastAPI, HTTPException, Depends, status
from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
from jose import JWTError, jwt
from pydantic import BaseModel
from typing import Optional, List
from datetime import datetime, timedelta
import hashlib
import json
ALGORITHM = "RS256"
SECRET_KEY = os.environ.get("JWT_SECRET_KEY") # RS256 private key
PUBLIC_KEY = os.environ.get("JWT_PUBLIC_KEY") # RS256 public key
MCP_PERMISSIONS = {
"read": ["tools:get", "resources:list", "prompts:read"],
"write": ["tools:call", "resources:write", "prompts:create"],
"admin": ["*"] # Full access
}
class MCPTokenClaims(BaseModel):
sub: str # User ID
org_id: str
roles: List[str]
mcp_scope: List[str]
exp: datetime
iat: datetime
jti: str # Token ID for revocation
class MCPResource(BaseModel):
type: str
name: str
actions: List[str]
security = HTTPBearer(auto_error=False)
async def verify_mcp_token(credentials: HTTPAuthorizationCredentials = Depends(security)):
if not credentials:
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail="MCP token required",
headers={"WWW-Authenticate": "Bearer realm=mcp-gateway"}
)
try:
payload = jwt.decode(
credentials.credentials,
PUBLIC_KEY,
algorithms=[ALGORITHM],
audience="mcp-gateway"
)
token_claims = MCPTokenClaims(**payload)
# Check expiration
if datetime.utcnow() > token_claims.exp:
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail="Token expired"
)
# Generate permission set
permissions = set()
for role in token_claims.roles:
permissions.update(MCP_PERMISSIONS.get(role, []))
return {
"user_id": token_claims.sub,
"org_id": token_claims.org_id,
"permissions": permissions,
"token_id": token_claims.jti
}
except JWTError as e:
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail=f"Invalid token: {str(e)}"
)
def require_mcp_permission(required_action: str):
async def check_permission(auth: dict = Depends(verify_mcp_token)):
if "*" in auth["permissions"] or required_action in auth["permissions"]:
return auth
raise HTTPException(
status_code=status.HTTP_403_FORBIDDEN,
detail=f"Permission denied: {required_action}"
)
return check_permission
Hệ Thống Audit Log Toàn Diện
Audit log là trái tim của compliance và security monitoring. MCP gateway cần capture:
- Full request/response bodies (sau khi sanitize PII)
- Token usage và cost attribution
- Latency metrics
- Error traces
- Security events (auth failures, permission denied)
mcp_audit/logger.py
import asyncio
import json
import hashlib
import structlog
from datetime import datetime, timezone
from typing import Any, Dict, Optional
from dataclasses import dataclass, asdict
from enum import Enum
import redis.asyncio as redis
import elasticsearch
@dataclass
class MCPAuditEvent:
event_id: str
timestamp: str
event_type: str
user_id: str
org_id: str
resource_type: str
resource_id: Optional[str]
action: str
request_hash: str
response_hash: str
token_count_input: int
token_count_output: int
cost_usd: float
latency_ms: float
status_code: int
ip_address: str
user_agent: str
mcp_version: str
error_detail: Optional[str]
metadata: Dict[str, Any]
class PIIRedactor:
"""Remove PII before logging"""
PATTERNS = {
"email": r'[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}',
"phone": r'\+?[1-9]\d{1,14}',
"credit_card": r'\d{4}[\s-]?\d{4}[\s-]?\d{4}[\s-]?\d{4}',
"ssn": r'\d{3}-\d{2}-\d{4}'
}
@classmethod
def redact(cls, data: str) -> str:
import re
result = data
for pii_type, pattern in cls.PATTERNS.items():
result = re.sub(pattern, f"[REDACTED_{pii_type.upper()}]", result)
return result
@classmethod
def hash_sensitive(cls, data: str) -> str:
return hashlib.sha256(data.encode()).hexdigest()[:16]
class AuditLogger:
def __init__(self, redis_url: str, es_url: str):
self.redis = redis.from_url(redis_url)
self.es = elasticsearch.Elasticsearch([es_url])
self.pii_redactor = PIIRedactor()
self.logger = structlog.get_logger()
async def log_mcp_event(
self,
event_type: str,
user_id: str,
org_id: str,
request_body: bytes,
response_body: bytes,
status_code: int,
latency_ms: float,
token_usage: Dict[str, int],
cost_usd: float,
ip_address: str,
error: Optional[Exception] = None
):
# Sanitize before logging
sanitized_request = self.pii_redactor.redact(request_body.decode('utf-8', errors='replace'))
sanitized_response = self.pii_redactor.redact(response_body.decode('utf-8', errors='replace'))
event = MCPAuditEvent(
event_id=f"{org_id}-{datetime.now(timezone.utc).strftime('%Y%m%d%H%M%S')}-{hashlib.md5(str(time.time()).encode()).hexdigest()[:8]}",
timestamp=datetime.now(timezone.utc).isoformat(),
event_type=event_type,
user_id=user_id,
org_id=org_id,
resource_type="mcp_tool" if "tools" in sanitized_request else "mcp_resource",
resource_id=self._extract_resource_id(sanitized_request),
action=self._extract_action(sanitized_request),
request_hash=hashlib.sha256(request_body).hexdigest(),
response_hash=hashlib.sha256(response_body).hexdigest(),
token_count_input=token_usage.get("input", 0),
token_count_output=token_usage.get("output", 0),
cost_usd=cost_usd,
latency_ms=latency_ms,
status_code=status_code,
ip_address=ip_address,
user_agent="MCP-Client/1.0",
mcp_version="1.0",
error_detail=str(error) if error else None,
metadata={
"redacted_request_sample": sanitized_request[:500],
"environment": os.environ.get("ENVIRONMENT", "production")
}
)
# Write to Redis for real-time alerting
await self._write_to_redis(event)
# Async write to Elasticsearch for analytics
asyncio.create_task(self._write_to_elasticsearch(event))
# Log to structlog for local debugging
self.logger.info(
"mcp_audit_event",
**asdict(event)
)
async def _write_to_redis(self, event: MCPAuditEvent):
key = f"mcp:audit:{event.org_id}:{event.timestamp[:10]}"
await self.redis.lpush(key, json.dumps(asdict(event)))
await self.redis.expire(key, 86400 * 30) # 30 days retention
async def _write_to_elasticsearch(self, event: MCPAuditEvent):
index = f"mcp-audit-{datetime.now().strftime('%Y.%m')}"
self.es.index(index=index, id=event.event_id, document=asdict(event))
async def query_audit_logs(
self,
org_id: str,
start_time: datetime,
end_time: datetime,
user_id: Optional[str] = None,
event_types: Optional[List[str]] = None
) -> list:
query = {
"bool": {
"must": [
{"term": {"org_id": org_id}},
{"range": {"timestamp": {"gte": start_time.isoformat(), "lte": end_time.isoformat()}}}
]
}
}
if user_id:
query["bool"]["must"].append({"term": {"user_id": user_id}})
if event_types:
query["bool"]["must"].append({"terms": {"event_type": event_types}})
result = self.es.search(
index="mcp-audit-*",
query=query,
sort=[{"timestamp": "desc"}],
size=1000
)
return [hit["_source"] for hit in result["hits"]["hits"]]
async def generate_compliance_report(
self,
org_id: str,
start_time: datetime,
end_time: datetime
) -> Dict[str, Any]:
logs = await self.query_audit_logs(org_id, start_time, end_time)
total_cost = sum(log["cost_usd"] for log in logs)
total_tokens = sum(log["token_count_input"] + log["token_count_output"] for log in logs)
error_count = sum(1 for log in logs if log["status_code"] >= 400)
# Group by user for billing attribution
user_costs = {}
for log in logs:
uid = log["user_id"]
user_costs[uid] = user_costs.get(uid, 0) + log["cost_usd"]
return {
"period": {"start": start_time.isoformat(), "end": end_time.isoformat()},
"total_requests": len(logs),
"total_tokens": total_tokens,
"total_cost_usd": round(total_cost, 4),
"error_rate": round(error_count / len(logs) * 100, 2) if logs else 0,
"avg_latency_ms": round(sum(log["latency_ms"] for log in logs) / len(logs), 2) if logs else 0,
"cost_by_user": user_costs,
"compliance_status": "PASS" if error_count / len(logs) < 0.01 else "REVIEW_REQUIRED"
}
Tích Hợp HolySheep AI Vào MCP Gateway
HolySheep AI cung cấp unified endpoint cho nhiều model với pricing cực kỳ cạnh tranh. Dưới đây là cách tích hợp vào MCP security gateway:
mcp_hybrid/holy_sheep_integration.py
import httpx
import asyncio
from typing import Dict, Any, Optional
from datetime import datetime
class HolySheepMCPGateway:
"""
HolySheep AI MCP Gateway Integration
base_url: https://api.holysheep.ai/v1
"""
def __init__(self, api_key: str):
self.api_key = api_key
self.base_url = "https://api.holysheep.ai/v1"
self.client = httpx.AsyncClient(
base_url=self.base_url,
headers={
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
},
timeout=httpx.Timeout(30.0, connect=5.0)
)
# Pricing map - Updated 2026
MODEL_PRICING = {
"gpt-4.1": {"input": 0.002, "output": 0.008, "unit": "per_1k"},
"claude-sonnet-4.5": {"input": 0.003, "output": 0.015, "unit": "per_1k"},
"gemini-2.5-flash": {"input": 0.00035, "output": 0.0025, "unit": "per_1k"},
"deepseek-v3.2": {"input": 0.0001, "output": 0.00042, "unit": "per_1k"}
}
async def chat_completions(
self,
model: str,
messages: list,
temperature: float = 0.7,
max_tokens: Optional[int] = None
) -> Dict[str, Any]:
"""
Unified chat completions endpoint
Auto-routes to optimal model based on cost/latency requirements
"""
start_time = asyncio.get_event_loop().time()
payload = {
"model": model,
"messages": messages,
"temperature": temperature
}
if max_tokens:
payload["max_tokens"] = max_tokens
response = await self.client.post("/chat/completions", json=payload)
response.raise_for_status()
result = response.json()
latency_ms = (asyncio.get_event_loop().time() - start_time) * 1000
# Calculate cost
usage = result.get("usage", {})
input_tokens = usage.get("prompt_tokens", 0)
output_tokens = usage.get("completion_tokens", 0)
pricing = self.MODEL_PRICING.get(model, {"input": 0, "output": 0})
cost_usd = (
input_tokens * pricing["input"] / 1000 +
output_tokens * pricing["output"] / 1000
)
return {
"id": result["id"],
"model": result["model"],
"content": result["choices"][0]["message"]["content"],
"usage": {
"input_tokens": input_tokens,
"output_tokens": output_tokens,
"total_tokens": input_tokens + output_tokens
},
"cost_usd": round(cost_usd, 6),
"latency_ms": round(latency_ms, 2),
"provider": "holy_sheep"
}
async def embeddings(
self,
model: str,
input_text: str
) -> Dict[str, Any]:
"""Generate embeddings via HolySheep"""
response = await self.client.post("/embeddings", json={
"model": model,
"input": input_text
})
response.raise_for_status()
return response.json()
async def stream_chat(
self,
model: str,
messages: list,
callback=None
):
"""
Streaming chat with cost tracking
Returns chunks via callback for real-time processing
"""
accumulated_content = ""
start_time = asyncio.get_event_loop().time()
async with self.client.stream(
"POST",
"/chat/completions",
json={
"model": model,
"messages": messages,
"stream": True
}
) as response:
async for line in response.aiter_lines():
if line.startswith("data: "):
data = line[6:]
if data == "[DONE]":
break
chunk = json.loads(data)
content = chunk["choices"][0]["delta"].get("content", "")
accumulated_content += content
if callback:
await callback(content, chunk)
latency_ms = (asyncio.get_event_loop().time() - start_time) * 1000
return {
"content": accumulated_content,
"latency_ms": round(latency_ms, 2)
}
Usage example in MCP context
async def mcp_tool_handler(user_request: Dict[str, Any], auth_context: Dict):
"""
Handle MCP tool invocation with HolySheep backend
"""
gateway = HolySheepMCPGateway(api_key=auth_context["api_key"])
# Route based on task complexity
task_type = classify_task(user_request["prompt"])
if task_type == "simple":
model = "gemini-2.5-flash" # Fast & cheap
elif task_type == "reasoning":
model = "deepseek-v3.2" # Best cost/performance
else:
model = "gpt-4.1" # Premium tasks
result = await gateway.chat_completions(
model=model,
messages=[{"role": "user", "content": user_request["prompt"]}],
temperature=user_request.get("temperature", 0.7)
)
# Log to audit system
audit_logger.log_mcp_event(
event_type="tool_invocation",
user_id=auth_context["user_id"],
org_id=auth_context["org_id"],
request_body=json.dumps(user_request).encode(),
response_body=json.dumps(result).encode(),
status_code=200,
latency_ms=result["latency_ms"],
token_usage=result["usage"],
cost_usd=result["cost_usd"],
ip_address=auth_context["ip_address"]
)
return result
Lỗi Thường Gặp và Cách Khắc Phục
1. Lỗi 401 Unauthorized - Token Validation Failed
Mô tả: JWT token không được validate, trả về HTTP 401 với message "Invalid token" hoặc "Token expired".
Nguyên nhân thường gặp:
- Token đã hết hạn (exp claim)
- Signature verification thất bại do sai public key
- Audience claim không match với "mcp-gateway"
- Clock skew giữa auth server và gateway
Mã khắc phục:
Fix: Token validation với retry logic và clock skew handling
from datetime import datetime, timezone, timedelta
class RobustTokenValidator:
def __init__(self, public_key: str, expected_audience: str = "mcp-gateway"):
self.public_key = public_key
self.expected_audience = expected_audience
# Allow 5 minutes clock skew
self.clock_skew_tolerance = timedelta(minutes=5)
def validate_token(self, token: str) -> Dict[str, Any]:
try:
# Decode without verification first to check claims
unverified = jwt.decode(token, options={"verify_signature": False})
# Check expiration with clock skew tolerance
exp = datetime.fromtimestamp(unverified["exp"], tz=timezone.utc)
now = datetime.now(timezone.utc)
if exp < now - self.clock_skew_tolerance:
raise TokenExpiredError(f"Token expired at {exp}")
# Verify signature
payload = jwt.decode(
token,
self.public_key,
algorithms=["RS256"],
audience=self.expected_audience,
options={"verify_exp": False} # Already checked above
)
return payload
except jwt.InvalidAudienceError:
# Try to find correct audience
raise TokenValidationError(
f"Invalid audience. Expected: {self.expected_audience}, "
f"Got: {unverified.get('aud')}"
)
except jwt.InvalidSignatureError:
# Fallback: check if using HS256
try:
return jwt.decode(token, self.public_key, algorithms=["HS256"])
except:
raise TokenValidationError("Signature verification failed")
except jwt.DecodeError as e:
raise TokenValidationError(f"Token decode error: {str(e)}")
Usage với retry
async def authenticate_request(request: Request, validator: RobustTokenValidator):
auth_header = request.headers.get("Authorization")
if not auth_header:
raise HTTPException(401, "Missing Authorization header")
scheme, token = auth_header.split(" ", 1)
if scheme.lower() != "bearer":
raise HTTPException(401, "Invalid auth scheme, expected Bearer")
try:
payload = validator.validate_token(token)
return payload
except TokenExpiredError:
# Try to refresh token
refresh_token = request.headers.get("X-Refresh-Token")
if refresh_token:
return await refresh_and_validate(refresh_token, validator)
raise HTTPException(401, "Token expired, refresh required")
2. Lỗi 429 Rate Limit Exceeded
Mô tả: Request bị reject với HTTP 429, thường kèm header "Retry-After: X".
Nguyên nhân:
- Vượt quá rate limit per user (10 requests/second)
- Vượt quá connection limit per IP (50 connections)
- Organization quota exceeded
Mã khắc phục:
Fix: Exponential backoff với Redis-based distributed rate limiting
import asyncio
import redis.asyncio as redis
from typing import Tuple
class DistributedRateLimiter:
def __init__(self, redis_url: str):
self.redis = redis.from_url(redis_url)
self.default_limits = {
"requests_per_second": 10,
"requests_per_minute": 500,
"requests_per_hour": 10000,
"concurrent_connections": 50
}
async def check_rate_limit(
self,
identifier: str,
limit_type: str = "user"
) -> Tuple[bool, Dict[str, Any]]:
"""
Returns (allowed, headers)
"""
now = await self.redis.time()
current_second = now[0]
current_minute = current_second // 60
current_hour = current_second // 3600
limits = self.default_limits
# Check per-second limit
second_key = f"ratelimit:{limit_type}:{identifier}:s:{current_second}"
second_count = await self.redis.incr(second_key)
await self.redis.expire(second_key, 2)
if second_count > limits["requests_per_second"]:
ttl = await self.redis.ttl(second_key)
return False, {
"X-RateLimit-Limit": str(limits["requests_per_second"]),
"X-RateLimit-Remaining": "0",
"X-RateLimit-Reset": str(current_second + ttl),
"Retry-After": str(ttl)
}
# Check per-minute limit
minute_key = f"ratelimit:{limit_type}:{identifier}:m:{current_minute}"
minute_count = await self.redis.incr(minute_key)
await self.redis.expire(minute_key, 120)
if minute_count > limits["requests_per_minute"]:
ttl = await self.redis.ttl(minute_key)
return False, {"Retry-After": str(ttl)}
# Check concurrent connections
conn_key = f"ratelimit:{limit_type}:{identifier}:conn"
current_conn = await self.redis.incr(conn_key)
await self.redis.expire(conn_key, 60)
if current_conn > limits["concurrent_connections"]:
return False, {"Retry-After": "1"}
remaining = limits["requests_per_second"] - second_count
return True, {
"X-RateLimit-Limit": str(limits["requests_per_second"]),
"X-RateLimit-Remaining": str(max(0, remaining)),
"X-RateLimit-Reset": str(current_second + 1)
}
async def rate_limited_request(request_func, identifier: str, max_retries: int = 3):
"""Execute request với automatic retry on rate limit"""
limiter = DistributedRateLimiter("redis://localhost:6379")
for attempt in range(max_retries):
allowed, headers = await limiter.check_rate_limit(identifier)
if not allowed:
retry_after = int(headers.get("Retry-After", 1))
# Exponential backoff
wait_time = retry_after * (2 ** attempt) + random.uniform(0, 0.1)
if attempt < max_retries - 1:
await asyncio.sleep(wait_time)
continue
else:
raise HTTPException(429, "Rate limit exceeded", headers=headers)
# Execute request
response = await request_func()
response.headers.update(headers)
return response
raise HTTPException(429, "Max retries exceeded")
3. Lỗi Audit Log Không Ghi Được Dữ Liệu
Mô tả: Events được tạo nhưng không xuất hiện trong Elasticsearch hoặc bị missing fields.
Nguyên nhân thường gặp:
- Elasticsearch index mapping không match schema
- Redis connection pool exhausted
- Async task bị dropped khi gateway crashes
- Data type mismatch (string vs number)
Mã khắc phục:
Fix: Resilient audit logging với dual-write và fallback
import asyncio
import json
from typing import Any, Dict, Optional
from contextlib import asynccontextmanager
import logging
class ResilientAuditLogger:
def __init__(self):
self.logger = logging.getLogger("audit")
self.fallback_queue = asyncio.Queue(maxsize=10000)
self._setup_elasticsearch_index()
def _setup_elasticsearch_index(self):
"""Ensure index exists with correct mapping"""
mapping = {
"mappings": {
"properties": {
"event_id": {"type": "keyword"},
"timestamp": {"type": "date"},
"event_type": {"type": "keyword"},
"user_id": {"type": "keyword"},
"org_id": {"type": "keyword"},
"resource_type": {"type": "keyword"},
"resource_id": {"type": "keyword"},
"action": {"type": "keyword"},
"request_hash": {"type": "keyword"},
"response_hash": {"type": "keyword"},
"token_count_input": {"type": "integer"},
"token_count_output": {"type": "integer"},
"cost_usd": {"type": "float"},
"latency_ms": {"type": "float"},
"status_code": {"type": "integer"},
"ip_address": {"type": "ip"},
"user_agent": {"type": "text"},
"mcp_version": {"type": "keyword"},
"error_detail": {"type": "text"},
"metadata": {"type": "object", "enabled": True}
}
},
"settings": {
"number_of_shards": 3,
"number_of_replicas": 1,
"refresh_interval": "5s"
}
}
# Create index if not exists
try:
self.es.indices.create(index="mcp-audit-*", body=mapping, ignore=400)
except Exception as e:
self.logger.error(f"Failed to create index: {e}")
async def log_event(self, event: MCPAuditEvent):
"""Async logging với guaranteed delivery"""
event_dict =