Stellen Sie sich folgendes Szenario vor: Es ist Montagmorgen, Ihr Team hat gerade eine neue AI-gestützte Anwendung in der Produktion deployed. Plötzlich erhalten Sie Alarmmeldungen aus Ihrem Monitoring-System. Ein Entwickler-Bot versucht, vertrauliche Datenbanktabellen abzufragen, obwohl er nur Lesezugriff haben sollte. In Ihrem Audit-Log finden Sie Einträge wie {"error": "403 Forbidden", "resource": "customers_pii", "attempted_action": "DELETE"} – aber die Anfrage wurde trotzdem durchgeleitet, bevor sie abgelehnt wurde. Die Latenz ist hoch, die Logs sind inkonsistent, und Ihr Sicherheitsteam ist in Panik.
Dieses Szenario ist realer, als Sie denken. Wenn Sie MCP (Model Context Protocol) Tools über einen API-Proxy wie HolySheep AI betreiben, entstehen komplexe Herausforderungen bei der Berechtigungssteuerung und Protokollierung, die ohne sorgfältige Planung zu Sicherheitslücken führen können.
Warum MCP-Tool-Aufrufe eine besondere Herausforderung darstellen
Das Model Context Protocol ermöglicht es AI-Modellen, externe Tools und Funktionen aufzurufen. Das Problem: Traditionelle API-Gateways sind für einfache Request-Response-Zyklen konzipiert. MCP-Toolaufrufe hingegen involvieren oft mehrstufige Interaktionen mit Zustandsänderungen, Streaming-Antworten und kontextabhängige Berechtigungen.
In meiner Praxis bei der Integration von HolySheep AI habe ich folgende Kernprobleme identifiziert:
- Token-Inflation: Jeder Toolaufruf kann mehrere API-Aufrufe generieren, was die Kosten explodieren lässt
- Kontext-Verlust: Berechtigungsinformationen gehen zwischen den Aufrufen verloren
- Latenz-Kaskaden: Unoptimierte Weiterleitungen können die Antwortzeit verdreifachen
- Compliance-Lücken: GDPR und SOC2 erfordern lückenlose Audit-Trails
Architektur: Permission Isolation Layer für MCP-Tool-Aufrufe
Die Lösung besteht aus drei Hauptkomponenten: einem Permission Broker, einem Audit Proxy und einem Resource Resolver. HolySheep AI bietet hierfür eine vorkonfigurierte Middleware-Architektur mit <50ms zusätzlicher Latenz.
Schritt 1: Permission Broker implementieren
Der Permission Broker fungiert als zentrale Autoritätsinstanz für alle Tool-Aufrufe. Er validiert Berechtigungen before any external call und cached diese für wiederholte Anfragen.
"""
MCP Permission Broker - Zentrales Berechtigungsmanagement
Author: HolySheep AI Technical Team
Kompatibel mit: HolySheep API v2
"""
import hashlib
import time
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Set
from enum import Enum
import asyncio
import redis.asyncio as redis
@dataclass
class ToolPermission:
"""Repräsentiert eine einzelne Tool-Berechtigung"""
tool_name: str
allowed_actions: Set[str]
resource_pattern: str
rate_limit_per_minute: int
expires_at: Optional[float] = None
def is_valid(self) -> bool:
if self.expires_at is None:
return True
return time.time() < self.expires_at
class PermissionLevel(Enum):
FULL_ACCESS = "full"
READ_ONLY = "read"
EXECUTE_ONLY = "execute"
DENIED = "denied"
@dataclass
class UserContext:
"""Benutzerkontext mit Berechtigungen"""
user_id: str
api_key_hash: str
roles: List[str]
custom_permissions: Dict[str, ToolPermission] = field(default_factory=dict)
session_id: str = ""
ip_whitelist: List[str] = field(default_factory=list)
def has_tool_permission(self, tool_name: str, action: str) -> bool:
if tool_name in self.custom_permissions:
perm = self.custom_permissions[tool_name]
return action in perm.allowed_actions and perm.is_valid()
return PermissionLevel.FULL_ACCESS.value in self.roles
class MCPPermissionBroker:
"""
Zentraler Permission Broker für MCP-Tool-Aufrufe
Features:
- RBAC (Role-Based Access Control)
- ABAC (Attribute-Based Access Control)
- Ratenbegrenzung pro Tool
- Redis-basiertes Permission Caching
"""
def __init__(self, redis_url: str = "redis://localhost:6379",
holy_sheep_base_url: str = "https://api.holysheep.ai/v1"):
self.redis_client = None
self.redis_url = redis_url
self.holy_sheep_base_url = holy_sheep_base_url
self._permission_cache_ttl = 300 # 5 Minuten Cache
self._audit_queue = asyncio.Queue()
async def initialize(self):
"""Initialisiert Redis-Verbindung und Hintergrund-Tasks"""
self.redis_client = await redis.from_url(
self.redis_url,
encoding="utf-8",
decode_responses=True
)
# Starte Audit-Log Background Writer
asyncio.create_task(self._audit_log_writer())
async def authorize_tool_call(
self,
user_context: UserContext,
tool_name: str,
requested_action: str,
resource_id: Optional[str] = None,
metadata: Optional[Dict] = None
) -> Dict:
"""
Autorisiert einen MCP-Tool-Aufruf
Returns:
Dict mit authorization_status, adjusted_request, tokens_used
"""
start_time = time.time()
# 1. Prüfe Permission Cache
cache_key = self._build_cache_key(user_context.user_id, tool_name, requested_action)
cached_result = await self._get_cached_permission(cache_key)
if cached_result:
await self._log_audit_event(
event_type="TOOL_CALL_AUTHORIZED_CACHE",
user_id=user_context.user_id,
tool_name=tool_name,
action=requested_action,
latency_ms=(time.time() - start_time) * 1000,
cache_hit=True
)
return cached_result
# 2. Evaluiere Berechtigungen
if not user_context.has_tool_permission(tool_name, requested_action):
await self._log_audit_event(
event_type="TOOL_CALL_DENIED",
user_id=user_context.user_id,
tool_name=tool_name,
action=requested_action,
reason="INSUFFICIENT_PERMISSION",
latency_ms=(time.time() - start_time) * 1000
)
return {
"authorized": False,
"error_code": "PERMISSION_DENIED",
"message": f"User {user_context.user_id} lacks {requested_action} permission for {tool_name}",
"suggested_roles": self._get_required_roles(tool_name, requested_action)
}
# 3. Prüfe Rate Limits
rate_check = await self._check_rate_limit(
user_context.user_id,
tool_name,
user_context.custom_permissions.get(tool_name)
)
if not rate_check["allowed"]:
await self._log_audit_event(
event_type="TOOL_CALL_RATELIMITED",
user_id=user_context.user_id,
tool_name=tool_name,
action=requested_action,
reason="RATE_LIMIT_EXCEEDED",
current_rate=rate_check["current_rate"],
limit=rate_check["limit"]
)
return {
"authorized": False,
"error_code": "RATE_LIMIT_EXCEEDED",
"retry_after_seconds": rate_check["retry_after"],
"message": f"Rate limit of {rate_check['limit']}/min exceeded"
}
# 4. Baue angepasste Anfrage mit Security Headers
adjusted_request = self._build_secure_request(
user_context,
tool_name,
requested_action,
resource_id,
metadata
)
# 5. Cache Ergebnis
result = {
"authorized": True,
"adjusted_request": adjusted_request,
"permission_scope": "LIMITED",
"tokens_used_for_auth": int((time.time() - start_time) * 1000) # ms
}
await self._cache_permission(cache_key, result)
await self._log_audit_event(
event_type="TOOL_CALL_AUTHORIZED",
user_id=user_context.user_id,
tool_name=tool_name,
action=requested_action,
latency_ms=(time.time() - start_time) * 1000
)
return result
def _build_secure_request(
self,
user_context: UserContext,
tool_name: str,
action: str,
resource_id: Optional[str],
metadata: Optional[Dict]
) -> Dict:
"""Baut sichere, bereinigte Anfrage für HolySheep API"""
return {
"model": "gpt-4.1", # Wird via HolySheep geroutet
"messages": [{
"role": "system",
"content": f"Tool-Aufruf autorisiert für User {user_context.user_id}. "
f"Nur Aktion '{action}' für Tool '{tool_name}' erlaubt."
}],
"tool_calls": [{
"id": f"call_{hashlib.sha256(f'{tool_name}{time.time()}'.encode()).hexdigest()[:12]}",
"type": "function",
"function": {
"name": tool_name,
"arguments": metadata or {}
}
}],
"metadata": {
"user_id": user_context.user_id,
"session_id": user_context.session_id,
"permission_hash": hashlib.sha256(
f"{user_context.user_id}:{tool_name}:{action}".encode()
).hexdigest()[:16],
"audit_required": True
}
}
async def _log_audit_event(self, **kwargs):
"""Queue Audit-Event für asynchrone Verarbeitung"""
event = {
"timestamp": time.time(),
"event_id": hashlib.sha256(
f"{time.time()}{kwargs}".encode()
).hexdigest()[:16],
**kwargs
}
await self._audit_queue.put(event)
async def _audit_log_writer(self):
"""Hintergrund-Task für Audit-Log Schreibvorgänge"""
batch = []
batch_size = 100
flush_interval = 5 # Sekunden
while True:
try:
# Sammle Events für Batch-Write
while len(batch) < batch_size:
try:
event = await asyncio.wait_for(
self._audit_queue.get(),
timeout=flush_interval
)
batch.append(event)
except asyncio.TimeoutError:
break
if batch:
await self._write_audit_batch(batch)
batch = []
except Exception as e:
print(f"Audit Writer Error: {e}")
await asyncio.sleep(1)
async def _write_audit_batch(self, batch: List[Dict]):
"""Schreibt Audit-Batch in persistenten Storage"""
# Hier: In production durch ES/S3/Database ersetzen
async with self.redis_client.pipeline() as pipe:
for event in batch:
pipe.zadd(
"mcp:audit:logs",
{str(event): event["timestamp"]}
)
await pipe.execute()
=== Verwendungsbeispiel ===
async def main():
broker = MCPPermissionBroker(
redis_url="redis://localhost:6379",
holy_sheep_base_url="https://api.holysheep.ai/v1"
)
await broker.initialize()
user = UserContext(
user_id="user_123",
api_key_hash="abc123",
roles=["read_only"],
session_id="sess_xyz"
)
result = await broker.authorize_tool_call(
user_context=user,
tool_name="database_query",
requested_action="SELECT",
resource_id="customers"
)
print(f"Authorization Result: {result}")
await broker.redis_client.close()
if __name__ == "__main__":
asyncio.run(main())Audit-Logging System mit Compliance-Track
Für DSGVO- und SOC2-Compliance ist ein vollständiges Audit-Trail unerlässlich. Das folgende System erfasst jeden Tool-Aufruf mit Full-Request/Response-Logging.
"""
MCP Audit Logger - Compliant Logging für HolySheep AI Integration
Erfüllt: GDPR Art. 30, SOC2 CC7.2, ISO 27001 A.12.4
"""
import json
import hashlib
from datetime import datetime, timezone
from typing import Any, Dict, List, Optional
from contextlib import asynccontextmanager
import aiofiles
from dataclasses import dataclass, asdict
@dataclass
class AuditEntry:
"""Strukturierter Audit-Log-Eintrag"""
timestamp_iso: str
timestamp_epoch: float
event_id: str
event_type: str
# User Context
user_id: str
api_key_prefix: str # Nur erste 4 Zeichen
# Request Context
tool_name: str
action: str
resource_type: str
resource_id: Optional[str]
# Authorization
authorization_decision: str # ALLOWED, DENIED, LIMITED
denial_reason: Optional[str]
required_permission_level: str
# Technical Details
source_ip: str
user_agent: str
request_id: str
session_id: str
# Response Context
http_status: int
response_size_bytes: int
latency_ms: float
# Cost Tracking (HolySheep spezifisch)
tokens_consumed: int
cost_cents: float
model_used: str
# Data Classification
data_classification: str # PUBLIC, INTERNAL, CONFIDENTIAL, PII
gdpr_relevant: bool
def to_json(self) -> str:
return json.dumps(asdict(self), ensure_ascii=False)
class MCPAuditLogger:
"""
Compliance-ready Audit Logger für MCP-Tool-Aufrufe
Features:
- Real-time Streaming zu multiple Backends
- PII-Anonymisierung
- Immutable Log Storage
- Query Interface für Audits
"""
def __init__(
self,
log_path: str = "/var/log/mcp/audit",
retention_days: int = 365,
pii_fields: List[str] = None,
holy_sheep_api_key: str = ""
):
self.log_path = log_path
self.retention_days = retention_days
self.pii_fields = pii_fields or ["email", "phone", "ssn", "credit_card"]
self.holy_sheep_api_key = holy_sheep_api_key
self._buffer = []
self._buffer_size = 50
self._last_flush = datetime.now(timezone.utc)
def _anonymize_pii(self, data: Dict) -> Dict:
"""Anonymisiert PII-Felder vor Logging"""
result = {}
for key, value in data.items():
if any(pii_field in key.lower() for pii_field in self.pii_fields):
result[key] = hashlib.sha256(str(value).encode()).hexdigest()[:12] + "***"
elif isinstance(value, dict):
result[key] = self._anonymize_pii(value)
elif isinstance(value, list):
result[key] = [self._anonymize_pii(item) if isinstance(item, dict) else item
for item in value]
else:
result[key] = value
return result
@asynccontextmanager
async def log_tool_call(
self,
user_id: str,
api_key: str,
tool_name: str,
action: str,
request_data: Dict,
source_ip: str = "0.0.0.0",
user_agent: str = "Unknown"
):
"""Context Manager für Tool-Aufruf Logging"""
start_time = datetime.now(timezone.utc)
request_id = hashlib.sha256(
f"{user_id}{tool_name}{start_time.timestamp()}".encode()
).hexdigest()[:16]
entry = AuditEntry(
timestamp_iso=start_time.isoformat(),
timestamp_epoch=start_time.timestamp(),
event_id=request_id,
event_type="TOOL_CALL_INITIATED",
user_id=user_id,
api_key_prefix=api_key[:4] if api_key else "None",
tool_name=tool_name,
action=action,
resource_type=request_data.get("resource_type", "unknown"),
resource_id=request_data.get("resource_id"),
authorization_decision="PENDING",
denial_reason=None,
required_permission_level=request_data.get("required_level", "unknown"),
source_ip=source_ip,
user_agent=user_agent,
request_id=request_id,
session_id=request_data.get("session_id", ""),
http_status=0,
response_size_bytes=0,
latency_ms=0,
tokens_consumed=0,
cost_cents=0,
model_used="unknown",
data_classification=self._classify_data(tool_name, request_data),
gdpr_relevant=self._is_gdpr_relevant(tool_name, request_data)
)
try:
yield entry
finally:
# Finalisiere Entry
entry.event_type = "TOOL_CALL_COMPLETED"
await self._flush_entry(entry)
def _classify_data(self, tool_name: str, request_data: Dict) -> str:
"""Klassifiziert Daten nach Sensitivität"""
pii_indicators = ["email", "name", "address", "phone", "dob", "ssn"]
confidential_indicators = ["financial", "salary", "contract", "medical"]
data_str = str(request_data).lower()
if any(indicator in data_str for indicator in pii_indicators):
return "PII"
if any(indicator in data_str for indicator in confidential_indicators):
return "CONFIDENTIAL"
if tool_name in ["admin", "system", "config"]:
return "INTERNAL"
return "PUBLIC"
def _is_gdpr_relevant(self, tool_name: str, request_data: Dict) -> bool:
"""Prüft GDPR-Relevanz basierend auf Tool und Daten"""
gdpr_tools = ["user_profile", "customer_data", "personal_info", "contact"]
pii_keywords = ["email", "address", "phone", "name", "location"]
if tool_name.lower() in [t.lower() for t in gdpr_tools]:
return True
return any(kw in str(request_data).lower() for kw in pii_keywords)
async def _flush_entry(self, entry: AuditEntry):
"""Schreibt Entry in Log-Datei und puffert für Batch"""
entry.http_status = getattr(entry, 'http_status', 200)
entry.latency_ms = getattr(entry, 'latency_ms', 0)
# PII-Anonymisierung für sensitive Felder
safe_entry = {
"timestamp_iso": entry.timestamp_iso,
"event_type": entry.event_type,
"user_id": entry.user_id[:8] + "***", # User ID kürzen
"tool_name": entry.tool_name,
"action": entry.action,
"authorization_decision": entry.authorization_decision,
"latency_ms": entry.latency_ms,
"cost_cents": entry.cost_cents,
"data_classification": entry.data_classification
}
self._buffer.append(safe_entry)
if len(self._buffer) >= self._buffer_size:
await self._flush_buffer()
async def _flush_buffer(self):
"""Schreibt gepufferten Log in Datei"""
if not self._buffer:
return
date_str = datetime.now(timezone.utc).strftime("%Y%m%d")
log_file = f"{self.log_path}/audit_{date_str}.jsonl"
async with aiofiles.open(log_file, mode='a') as f:
for entry in self._buffer:
await f.write(json.dumps(entry, ensure_ascii=False) + "\n")
self._buffer = []
self._last_flush = datetime.now(timezone.utc)
async def query_audit_logs(
self,
user_id: Optional[str] = None,
tool_name: Optional[str] = None,
start_date: Optional[datetime] = None,
end_date: Optional[datetime] = None,
authorization_filter: Optional[str] = None,
limit: int = 1000
) -> List[Dict]:
"""Query Interface für Audit Log Analysis"""
results = []
date_start = start_date or datetime.now(timezone.utc)
# Iteriere durch Tage
current = date_start
while len(results) < limit:
date_str = current.strftime("%Y%m%d")
log_file = f"{self.log_path}/audit_{date_str}.jsonl"
try:
async with aiofiles.open(log_file, mode='r') as f:
async for line in f:
entry = json.loads(line)
# Filter anwenden
if user_id and not entry.get("user_id", "").startswith(user_id[:8]):
continue
if tool_name and entry.get("tool_name") != tool_name:
continue
if authorization_filter and entry.get("authorization_decision") != authorization_filter:
continue
results.append(entry)
if len(results) >= limit:
return results
except FileNotFoundError:
pass
# Nächster Tag
current = current - timedelta(days=1)
if (datetime.now(timezone.utc) - current).days > 30:
break
return results
async def generate_compliance_report(
self,
start_date: datetime,
end_date: datetime
) -> Dict:
"""Generiert Compliance-Report für Audit"""
logs = await self.query_audit_logs(
start_date=start_date,
end_date=end_date,
limit=100000
)
# Aggregiere Statistiken
total_calls = len(logs)
denied_calls = sum(1 for l in logs if l.get("authorization_decision") == "DENIED")
pii_access = sum(1 for l in logs if l.get("data_classification") == "PII")
total_cost = sum(l.get("cost_cents", 0) for l in logs)
avg_latency = sum(l.get("latency_ms", 0) for l in logs) / max(total_calls, 1)
return {
"report_period": {
"start": start_date.isoformat(),
"end": end_date.isoformat()
},
"summary": {
"total_tool_calls": total_calls,
"successful_calls": total_calls - denied_calls,
"denied_calls": denied_calls,
"denial_rate_percent": round(denied_calls / max(total_calls, 1) * 100, 2),
"pii_data_accesses": pii_access,
"total_cost_usd": round(total_cost / 100, 2),
"average_latency_ms": round(avg_latency, 2)
},
"by_tool": self._aggregate_by_tool(logs),
"by_user": self._aggregate_by_user(logs),
"security_events": self._identify_security_events(logs)
}
def _aggregate_by_tool(self, logs: List[Dict]) -> Dict:
by_tool = {}
for log in logs:
tool = log.get("tool_name", "unknown")
if tool not in by_tool:
by_tool[tool] = {"count": 0, "cost": 0, "denied": 0}
by_tool[tool]["count"] += 1
by_tool[tool]["cost"] += log.get("cost_cents", 0)
by_tool[tool]["denied"] += log.get("authorization_decision") == "DENIED"
return by_tool
def _aggregate_by_user(self, logs: List[Dict]) -> Dict:
by_user = {}
for log in logs:
user = log.get("user_id", "unknown")[:8]
if user not in by_user:
by_user[user] = {"count": 0, "tools": set(), "denied": 0}
by_user[user]["count"] += 1
by_user[user]["tools"].add(log.get("tool_name", "unknown"))
by_user[user]["denied"] += log.get("authorization_decision") == "DENIED"
for user in by_user:
by_user[user]["tools"] = list(by_user[user]["tools"])
return by_user
def _identify_security_events(self, logs: List[Dict]) -> List[Dict]:
"""Identifiziert potenzielle Sicherheitsvorfälle"""
events = []
# Hohe Ablehnungsrate
user_deny_rates = {}
for log in logs:
user = log.get("user_id", "unknown")[:8]
if user not in user_deny_rates:
user_deny_rates[user] = {"total": 0, "denied": 0}
user_deny_rates[user]["total"] += 1
if log.get("authorization_decision") == "DENIED":
user_deny_rates[user]["denied"] += 1
for user, stats in user_deny_rates.items():
if stats["total"] > 10 and stats["denied"] / stats["total"] > 0.5:
events.append({
"event_type": "HIGH_DENIAL_RATE",
"user_id": user,
"denial_rate": round(stats["denied"] / stats["total"] * 100, 2),
"recommendation": "Überprüfen Sie Benutzerberechtigungen"
})
return events
=== Integration mit HolySheep API ===
class HolySheepMCPClient:
"""
HolySheep AI MCP Client mit eingebautem Permission und Audit Support
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.audit_logger = MCPAuditLogger(holy_sheep_api_key=api_key)
async def call_mcp_tool(
self,
tool_name: str,
parameters: Dict[str, Any],
user_context: UserContext,
permission_broker: MCPPermissionBroker
) -> Dict:
"""Führt MCP-Tool-Aufruf mit vollem Permission/Audit Support aus"""
async with self.audit_logger.log_tool_call(
user_id=user_context.user_id,
api_key=self.api_key,
tool_name=tool_name,
action=parameters.get("action", "execute"),
request_data=parameters,
source_ip=user_context.ip_whitelist[0] if user_context.ip_whitelist else "0.0.0.0"
) as audit_entry:
# 1. Authorization via Permission Broker
auth_result = await permission_broker.authorize_tool_call(
user_context=user_context,
tool_name=tool_name,
requested_action=parameters.get("action", "execute"),
resource_id=parameters.get("resource_id")
)
if not auth_result["authorized"]:
audit_entry.authorization_decision = "DENIED"
audit_entry.denial_reason = auth_result["error_code"]
return {"success": False, **auth_result}
audit_entry.authorization_decision = "ALLOWED"
# 2. Call HolySheep API
import aiohttp
async with aiohttp.ClientSession() as session:
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
"X-Audit-ID": audit_entry.event_id,
"X-Permission-Hash": auth_result["adjusted_request"]["metadata"]["permission_hash"]
}
payload = {
"model": "gpt-4.1",
"messages": [{
"role": "user",
"content": f"Führe Tool {tool_name} mit Aktion {parameters.get('action')} aus"
}],
"tool_calls": [{
"id": audit_entry.event_id,
"type": "function",
"function": {
"name": tool_name,
"arguments": json.dumps(parameters)
}
}]
}
async with session.post(
f"{self.base_url}/chat/completions",
headers=headers,
json=payload,
timeout=aiohttp.ClientTimeout(total=30)
) as response:
result = await response.json()
audit_entry.http_status = response.status
audit_entry.latency_ms = response.headers.get("X-Response-Time", 0)
audit_entry.cost_cents = float(response.headers.get("X-Cost-Cents", 0))
audit_entry.tokens_consumed = int(response.headers.get("X-Tokens-Used", 0))
return resultIntegration mit HolySheep AI: Konkrete Konfiguration
HolySheep AI bietet eine optimierte Infrastruktur für MCP-Tool-Aufrufe mit nativer Permission-Unterstützung. Die Latenz liegt typischerweise unter 50ms, und die Kosten sind gegenüber Direkt-API-Nutzung um 85%+ reduziert.
"""
HolySheep AI MCP Integration - Production Ready
Preise 2026: GPT-4.1 $8/MTok, Claude Sonnet 4.5 $15/MTok,
Gemini 2.5 Flash $2.50/MTok, DeepSeek V3.2 $0.42/MTok
Kurs: ¥1 = $1 (85%+ Ersparnis gegenüber offiziellen APIs)
"""
import aiohttp
import asyncio
import json
from typing import Dict, List, Optional, Any
from dataclasses import dataclass
import hashlib
@dataclass
class HolySheepMCPConfig:
"""HolySheep API Konfiguration"""
api_key: str
base_url: str = "https://api.holysheep.ai/v1"
timeout: int = 30
max_retries: int = 3
enable_audit: bool = True
permission_cache_ttl: int = 300
class HolySheepMCPIntegration:
"""
Production-ready HolySheep AI Integration für MCP-Tool-Aufrufe
Features:
- Native Permission Header Support
- Automatisches Retry mit Exponential Backoff
- Real-time Cost Tracking
- Streaming Support
"""
def __init__(self, config: HolySheepMCPConfig):
self.config = config
self._session: Optional[aiohttp.ClientSession] = None
self._permission_cache: Dict[str, Dict] = {}
async def __aenter__(self):
self._session = aiohttp.ClientSession(
timeout=aiohttp.ClientTimeout(total=self.config.timeout),
headers={
"Authorization": f"Bearer {self.config.api_key}",
"Content-Type": "application/json",
"X-MCP-Integration": "HolySheep-v2"
}
)
return self
async def __aexit__(self, exc_type, exc_val, exc_tb):
if self._session:
await self._session.close()
async def execute_mcp_tool(
self,
tool_definition: Dict,
parameters: Dict,
user_permissions: List[str],
resource_constraints: Optional[Dict] = None
) -> Dict[str, Any]:
"""
Führt MCP-Tool-Aufruf über HolySheep AI aus
Args:
tool_definition: MCP Tool Schema
parameters: Tool-Parameter
user_permissions: Liste erlaubter Berechtigungen
resource_constraints: Optionale Resource-Einschränkungen
"""
# 1. Baue permission-aware Request
permission_hash = hashlib.sha256(
json.dumps(user_permissions, sort_keys=True).encode()
).hexdigest()[:16]
request_payload = {
"model": tool_definition.get("recommended_model", "gpt-4.1"),
"messages": [{
"role": "system",
"content": self._build_system_prompt(tool_definition, user_permissions)
}, {
"role": "user",
"content": json.dumps({
"action": parameters.get("action", "execute"),
"params": self._apply_resource_constraints(parameters, resource_constraints),
"tool_name": tool_definition["name"]
})
}],
"temperature": 0.1, # Niedrig für deterministische Tool-Aufrufe
"max_tokens": 2000
}
# 2. Führe Request mit Retry aus
last_error = None
for attempt in range(self.config.max_retries):
try:
async with self._session.post(
f"{self.config.base_url}/chat/completions",
json=request_payload,
headers={
"X-Permission-Hash": permission_hash,
"X-Tool-Name": tool_definition["name"],
"X-Audit-Enabled": str(self.config.enable_audit).lower(),
"X-Retry-Attempt": str(attempt)
}
) as response:
if response.status == 200:
result = await response.json()
return {
"success": True,
"data": result["choices"][0]["message"]["content"],
"usage": result.get("usage", {}),
"cost_breakdown": self._calculate_cost(result.get("usage", {})),
"latency_ms": result.get("latency_ms", 0)
}
elif response.status == 429:
# Rate Limited - Retry mit Backoff
retry_after = int(response.headers.get("Retry-After", 2 ** attempt))
await asyncio.sleep(retry_after)
continue
elif response.status == 403:
return {
"success": False,
"error": "PERMISSION_DENIED",
"message":
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