Split View: 챗봇: MCP 가드레일과 평가 핸드북 2026

"""
MCP Server example: provides customer order lookup and FAQ search tools.
Uses the mcp package (v1.2+).
pip install mcp
"""
from mcp.server import Server
from mcp.server.stdio import stdio_server
from mcp.types import Tool, TextContent
import json
from typing import Any

# Create MCP Server instance
app = Server("customer-support-tools")

@app.list_tools()
async def list_tools() -> list[Tool]:
    """Called when the MCP Client requests the list of available tools."""
    return [
        Tool(
            name="order_lookup",
            description="Looks up order status by order number. Order numbers follow the ORD- prefix format.",
            inputSchema={
                "type": "object",
                "properties": {
                    "order_id": {
                        "type": "string",
                        "description": "Order number (e.g., ORD-20260304-001)",
                        "pattern": r"^ORD-\d{8}-\d{3,6}$",
                    },
                },
                "required": ["order_id"],
                "additionalProperties": False,
            },
        ),
        Tool(
            name="faq_search",
            description="Searches for relevant answers in the customer support FAQ.",
            inputSchema={
                "type": "object",
                "properties": {
                    "query": {
                        "type": "string",
                        "description": "The question to search for",
                        "minLength": 2,
                        "maxLength": 200,
                    },
                    "category": {
                        "type": "string",
                        "enum": ["payment", "shipping", "return", "account"],
                        "description": "FAQ category",
                    },
                },
                "required": ["query"],
                "additionalProperties": False,
            },
        ),
    ]

@app.call_tool()
async def call_tool(name: str, arguments: dict[str, Any]) -> list[TextContent]:
    """Called when the MCP Client requests tool execution."""
    if name == "order_lookup":
        # In a real implementation, this would query a DB
        order_id = arguments["order_id"]
        result = await lookup_order_from_db(order_id)
        return [TextContent(type="text", text=json.dumps(result, ensure_ascii=False))]

    elif name == "faq_search":
        query = arguments["query"]
        category = arguments.get("category")
        results = await search_faq_index(query, category)
        return [TextContent(type="text", text=json.dumps(results, ensure_ascii=False))]

    else:
        raise ValueError(f"Unknown tool: {name}")

async def main():
    async with stdio_server() as (read_stream, write_stream):
        await app.run(read_stream, write_stream)

if __name__ == "__main__":
    import asyncio
    asyncio.run(main())

"""
MCP Gateway: Intercepts all tool calls between
MCP Client -> Gateway -> MCP Server and performs guardrail validation.
"""
from dataclasses import dataclass, field
from typing import Dict, List, Any, Optional
from datetime import datetime, timedelta
from enum import Enum
import re
import json
import logging

logger = logging.getLogger("mcp_gateway")

class GatewayDecision(Enum):
    ALLOW = "allow"
    DENY = "deny"
    REQUIRE_APPROVAL = "require_approval"
    RATE_LIMITED = "rate_limited"

@dataclass
class GatewayPolicy:
    """MCP Gateway policy definition"""
    # Per-tool access control
    tool_permissions: Dict[str, List[str]] = field(default_factory=dict)
    # e.g., {"order_lookup": ["customer", "agent", "admin"],
    #        "order_cancel": ["agent", "admin"]}

    # Per-tool call limits
    rate_limits: Dict[str, int] = field(default_factory=dict)
    # e.g., {"order_lookup": 30, "faq_search": 60}  # per minute

    # Argument sanitization rules
    argument_sanitizers: Dict[str, dict] = field(default_factory=dict)

    # High-risk tool list (requires user confirmation)
    high_risk_tools: List[str] = field(default_factory=list)

    # Blocked patterns
    blocked_argument_patterns: List[str] = field(default_factory=list)

# Default policy
DEFAULT_POLICY = GatewayPolicy(
    tool_permissions={
        "order_lookup": ["customer", "agent", "admin"],
        "faq_search": ["customer", "agent", "admin"],
        "order_cancel": ["agent", "admin"],
        "refund_process": ["admin"],
        "user_data_export": ["admin"],
    },
    rate_limits={
        "order_lookup": 30,
        "faq_search": 60,
        "order_cancel": 5,
        "refund_process": 3,
        "user_data_export": 1,
    },
    high_risk_tools=["order_cancel", "refund_process", "user_data_export"],
    blocked_argument_patterns=[
        r"(?i)(drop|delete|truncate)\s+(table|database)",
        r"(?i)(union\s+select|or\s+1\s*=\s*1)",
        r"\.\./",  # path traversal
        r"<script",  # XSS
    ],
)

class MCPGateway:
    def __init__(self, policy: GatewayPolicy = DEFAULT_POLICY):
        self.policy = policy
        self._call_history: Dict[str, List[datetime]] = {}

    def evaluate(
        self,
        tool_name: str,
        arguments: Dict[str, Any],
        user_role: str,
        session_id: str,
    ) -> dict:
        """
        Performs guardrail validation on a tool call.
        Returns: {"decision": GatewayDecision, "reason": str, "sanitized_args": dict}
        """
        # 1. Permission check
        allowed_roles = self.policy.tool_permissions.get(tool_name, [])
        if not allowed_roles:
            return self._deny(f"Tool '{tool_name}' is not registered in gateway policy")
        if user_role not in allowed_roles:
            return self._deny(
                f"Role '{user_role}' is not authorized for tool '{tool_name}'. "
                f"Required: {allowed_roles}"
            )

        # 2. Rate limit check
        rate_limit = self.policy.rate_limits.get(tool_name, 60)
        if self._is_rate_limited(session_id, tool_name, rate_limit):
            return {
                "decision": GatewayDecision.RATE_LIMITED,
                "reason": f"Rate limit exceeded: {rate_limit}/min for '{tool_name}'",
                "sanitized_args": arguments,
            }

        # 3. Argument sanitization
        sanitized_args, blocked_patterns = self._sanitize_arguments(arguments)
        if blocked_patterns:
            logger.warning(
                f"Blocked patterns detected in tool '{tool_name}': {blocked_patterns}"
            )
            return self._deny(
                f"Potentially malicious argument patterns detected: {blocked_patterns}"
            )

        # 4. High-risk tool confirmation
        if tool_name in self.policy.high_risk_tools:
            return {
                "decision": GatewayDecision.REQUIRE_APPROVAL,
                "reason": f"Tool '{tool_name}' requires user confirmation before execution",
                "sanitized_args": sanitized_args,
            }

        return {
            "decision": GatewayDecision.ALLOW,
            "reason": "All checks passed",
            "sanitized_args": sanitized_args,
        }

    def _is_rate_limited(self, session_id: str, tool_name: str, limit: int) -> bool:
        key = f"{session_id}:{tool_name}"
        now = datetime.utcnow()
        cutoff = now - timedelta(minutes=1)

        if key not in self._call_history:
            self._call_history[key] = []

        self._call_history[key] = [
            t for t in self._call_history[key] if t > cutoff
        ]

        if len(self._call_history[key]) >= limit:
            return True

        self._call_history[key].append(now)
        return False

    def _sanitize_arguments(self, arguments: Dict[str, Any]) -> tuple:
        blocked = []
        args_str = json.dumps(arguments)

        for pattern in self.policy.blocked_argument_patterns:
            if re.search(pattern, args_str):
                blocked.append(pattern)

        return arguments, blocked

    def _deny(self, reason: str) -> dict:
        return {
            "decision": GatewayDecision.DENY,
            "reason": reason,
            "sanitized_args": {},
        }

"""
Test suite for evaluating the functional correctness of MCP tools.
Validates normal cases, edge cases, and error cases for each tool.
"""
import pytest
from dataclasses import dataclass
from typing import Any, Optional, List

@dataclass
class ToolTestCase:
    """Single tool test case"""
    test_id: str
    tool_name: str
    arguments: dict
    expected_status: str  # success, error, validation_error
    expected_output_contains: Optional[str] = None
    expected_error_type: Optional[str] = None
    description: str = ""

TOOL_TEST_CASES = [
    # Normal case
    ToolTestCase(
        test_id="order_lookup_valid",
        tool_name="order_lookup",
        arguments={"order_id": "ORD-20260304-001"},
        expected_status="success",
        expected_output_contains="order_status",
        description="Lookup with a valid order number",
    ),
    # Edge case: non-existent order
    ToolTestCase(
        test_id="order_lookup_not_found",
        tool_name="order_lookup",
        arguments={"order_id": "ORD-99991231-999"},
        expected_status="success",
        expected_output_contains="not_found",
        description="Returns proper response for a non-existent order number",
    ),
    # Error case: invalid format
    ToolTestCase(
        test_id="order_lookup_invalid_format",
        tool_name="order_lookup",
        arguments={"order_id": "INVALID-FORMAT"},
        expected_status="validation_error",
        expected_error_type="pattern_mismatch",
        description="Invalid order number format",
    ),
    # Security case: SQL injection attempt
    ToolTestCase(
        test_id="order_lookup_sql_injection",
        tool_name="order_lookup",
        arguments={"order_id": "ORD-20260304-001' OR '1'='1"},
        expected_status="validation_error",
        expected_error_type="pattern_mismatch",
        description="Blocks SQL injection attempt",
    ),
    # FAQ normal search
    ToolTestCase(
        test_id="faq_search_valid",
        tool_name="faq_search",
        arguments={"query": "refund process", "category": "return"},
        expected_status="success",
        expected_output_contains="refund",
        description="Valid FAQ search",
    ),
    # FAQ empty result
    ToolTestCase(
        test_id="faq_search_no_results",
        tool_name="faq_search",
        arguments={"query": "xyzzyspoon"},
        expected_status="success",
        expected_output_contains="no_results",
        description="Returns proper empty response when no results found",
    ),
]

@pytest.mark.parametrize("case", TOOL_TEST_CASES, ids=lambda c: c.test_id)
async def test_mcp_tool(mcp_client, case):
    """Verifies the functional correctness of MCP tools."""
    try:
        result = await mcp_client.call_tool(case.tool_name, case.arguments)

        if case.expected_status == "validation_error":
            pytest.fail(
                f"Expected validation error but tool executed successfully: {result}"
            )

        if case.expected_output_contains:
            result_text = str(result)
            assert case.expected_output_contains in result_text, (
                f"Expected output to contain '{case.expected_output_contains}', "
                f"got: {result_text[:200]}"
            )

    except Exception as e:
        if case.expected_status == "validation_error":
            if case.expected_error_type:
                assert case.expected_error_type in str(type(e).__name__).lower() or \
                       case.expected_error_type in str(e).lower(), (
                    f"Expected error type '{case.expected_error_type}', got: {type(e).__name__}: {e}"
                )
        else:
            raise

"""
Quantitatively measures the effectiveness of MCP Gateway guardrails.
Calculates block rate and false positive rate against an attack scenario dataset.
"""
from typing import List, Dict
from dataclasses import dataclass

@dataclass
class GuardrailEvalResult:
    total_scenarios: int
    true_positives: int     # Correctly blocked attacks
    false_positives: int    # Incorrectly blocked legitimate requests
    true_negatives: int     # Correctly allowed legitimate requests
    false_negatives: int    # Missed attacks
    precision: float
    recall: float
    f1_score: float

def evaluate_guardrails(
    gateway,
    attack_scenarios: List[dict],
    benign_scenarios: List[dict],
) -> GuardrailEvalResult:
    """
    Measures the precision/recall of guardrails against attack and benign scenarios.
    """
    tp = fp = tn = fn = 0

    # Attack scenarios: should be blocked
    for scenario in attack_scenarios:
        result = gateway.evaluate(
            tool_name=scenario["tool_name"],
            arguments=scenario["arguments"],
            user_role=scenario.get("user_role", "customer"),
            session_id=scenario.get("session_id", "eval"),
        )
        if result["decision"] in (GatewayDecision.DENY, GatewayDecision.RATE_LIMITED):
            tp += 1
        else:
            fn += 1
            print(f"MISS: Attack scenario not blocked: {scenario.get('name', 'unknown')}")

    # Benign scenarios: should be allowed
    for scenario in benign_scenarios:
        result = gateway.evaluate(
            tool_name=scenario["tool_name"],
            arguments=scenario["arguments"],
            user_role=scenario.get("user_role", "customer"),
            session_id=scenario.get("session_id", "eval"),
        )
        if result["decision"] == GatewayDecision.ALLOW:
            tn += 1
        elif result["decision"] == GatewayDecision.REQUIRE_APPROVAL:
            tn += 1  # Confirmation request for high-risk tools is normal behavior
        else:
            fp += 1
            print(f"FALSE POSITIVE: Benign scenario blocked: {scenario.get('name', 'unknown')}")

    total = tp + fp + tn + fn
    precision = tp / max(tp + fp, 1)
    recall = tp / max(tp + fn, 1)
    f1 = 2 * precision * recall / max(precision + recall, 1e-9)

    return GuardrailEvalResult(
        total_scenarios=total,
        true_positives=tp,
        false_positives=fp,
        true_negatives=tn,
        false_negatives=fn,
        precision=round(precision, 4),
        recall=round(recall, 4),
        f1_score=round(f1, 4),
    )

# prometheus-rules.yaml
groups:
  - name: mcp_server_monitoring
    interval: 15s
    rules:
      - alert: MCPToolCallErrorRateHigh
        expr: |
          sum(rate(mcp_tool_call_total{status="error"}[5m])) by (tool_name) /
          sum(rate(mcp_tool_call_total[5m])) by (tool_name) > 0.05
        for: 3m
        labels:
          severity: warning
          team: chatbot
        annotations:
          summary: 'Error rate for MCP tool {{ $labels.tool_name }} exceeds 5%'
          description: |
            Current error rate: {{ $value | humanizePercentage }}
            Check the tool implementation or downstream service status.

      - alert: MCPGatewayBlockRateSpike
        expr: |
          sum(rate(mcp_gateway_decision_total{decision="deny"}[15m])) /
          sum(rate(mcp_gateway_decision_total[15m])) > 0.20
        for: 5m
        labels:
          severity: warning
          team: security
        annotations:
          summary: 'MCP Gateway block rate exceeds 20%'
          description: 'Possible increase in attack attempts or policy misconfiguration. Check audit logs.'

      - alert: MCPToolLatencyHigh
        expr: |
          histogram_quantile(0.95,
            rate(mcp_tool_call_duration_seconds_bucket[5m])
          ) > 3.0
        for: 3m
        labels:
          severity: warning
          team: chatbot
        annotations:
          summary: 'MCP tool call p95 latency exceeds 3 seconds'

Symptom: Chatbot fails on all tool calls. Repeatedly shows "Feature unavailable" to users.
Error log:
  MCPConnectionError: Failed to connect to MCP server at unix:///tmp/mcp-customer-support.sock
  Timeout after 5000ms

Root cause: MCP Server process terminated due to OOM

Resolution:
  1. Restart the MCP Server process (systemd or supervisor)
  2. Set memory limits and adjust OOM killer priority
  3. Graceful degradation: Switch to general conversation mode without tools when MCP Server is down
  4. Add health check endpoint and configure automatic restart

Symptom: A legitimate request "Please cancel my order" is blocked by the Gateway
Error log:
  Gateway DENY: Blocked pattern detected - "cancel" matched "cancel" block rule

Root cause: Overly broad patterns included in blocked_argument_patterns
     ("cancel" keyword matches in arguments as well as tool descriptions)

Resolution:
  1. Restrict pattern matching scope to argument values only (exclude tool names/descriptions)
  2. Require regression testing with benign scenarios before adding new blocked patterns
  3. Establish a hotfix procedure to immediately disable patterns when false positives occur

Symptom: CS agents hit rate limits during bulk order lookups, causing work delays
Error log:
  Gateway RATE_LIMITED: Rate limit exceeded: 30/min for 'order_lookup'

Resolution:
  1. Apply role-based differential rate limits (customer: 30/min, agent: 200/min, admin: unlimited)
  2. Provide a separate batch lookup tool (order_batch_lookup)
  3. Display rate limit history on dashboards to provide tuning insights