本實驗深入探討如何建立高級的 MCP 工具，這些工具能為 AI 助手提供強大的資料庫查詢功能、結構檢視能力以及分析功能。您將學習如何構建既強大又安全的工具，並具備全面的錯誤處理和性能優化。
MCP 工具是 AI 助手與您的資料系統之間的介面。設計良好的工具能提供結構化且經過驗證的複雜操作，同時保持安全性和性能。本實驗涵蓋了工具開發的完整生命周期，從設計到部署。
我們的零售 MCP 伺服器實現了一套全面的工具，能夠自然語言查詢銷售數據、產品目錄以及商業分析，同時保持嚴格的安全邊界和最佳性能。
完成本實驗後，您將能夠：
- 設計 具有複雜參數驗證的高級 MCP 工具
- 實現 具有 SQL 注入保護的安全資料庫查詢工具
- 創建 用於動態查詢的結構檢視功能
- 構建 用於商業智能的自定義分析工具
- 應用 全面的錯誤處理和優雅降級
- 優化 工具性能以適應生產工作負載
# mcp_server/tools/base.py
"""
Base classes and patterns for MCP tool development.
"""
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional, Union
from dataclasses import dataclass
from enum import Enum
import asyncio
import time
import logging
from contextlib import asynccontextmanager
logger = logging.getLogger(__name__)
class ToolCategory(Enum):
"""Tool categorization for organization and discovery."""
DATABASE_QUERY = "database_query"
SCHEMA_INTROSPECTION = "schema_introspection"
ANALYTICS = "analytics"
UTILITY = "utility"
ADMINISTRATIVE = "administrative"
@dataclass
class ToolResult:
"""Standardized tool result structure."""
success: bool
data: Any = None
error: Optional[str] = None
metadata: Optional[Dict[str, Any]] = None
execution_time_ms: Optional[float] = None
row_count: Optional[int] = None
class BaseTool(ABC):
"""Abstract base class for all MCP tools."""
def __init__(self, name: str, description: str, category: ToolCategory):
self.name = name
self.description = description
self.category = category
self.call_count = 0
self.total_execution_time = 0.0
@abstractmethod
async def execute(self, **kwargs) -> ToolResult:
"""Execute the tool with given parameters."""
pass
@abstractmethod
def get_input_schema(self) -> Dict[str, Any]:
"""Get JSON schema for tool input validation."""
pass
async def call(self, **kwargs) -> ToolResult:
"""Wrapper for tool execution with metrics and error handling."""
start_time = time.time()
self.call_count += 1
try:
# Validate input parameters
self._validate_input(kwargs)
# Log tool execution
logger.info(
f"Executing tool: {self.name}",
extra={
'tool_name': self.name,
'tool_category': self.category.value,
'parameters': self._sanitize_parameters(kwargs)
}
)
# Execute the tool
result = await self.execute(**kwargs)
# Record execution time
execution_time = (time.time() - start_time) * 1000
result.execution_time_ms = execution_time
self.total_execution_time += execution_time
# Log success
logger.info(
f"Tool execution completed: {self.name}",
extra={
'tool_name': self.name,
'execution_time_ms': execution_time,
'success': result.success,
'row_count': result.row_count
}
)
return result
except Exception as e:
execution_time = (time.time() - start_time) * 1000
logger.error(
f"Tool execution failed: {self.name}",
extra={
'tool_name': self.name,
'execution_time_ms': execution_time,
'error': str(e)
},
exc_info=True
)
return ToolResult(
success=False,
error=f"Tool execution failed: {str(e)}",
execution_time_ms=execution_time
)
def _validate_input(self, kwargs: Dict[str, Any]):
"""Validate input parameters against schema."""
schema = self.get_input_schema()
required_props = schema.get('required', [])
properties = schema.get('properties', {})
# Check required parameters
missing_required = [prop for prop in required_props if prop not in kwargs]
if missing_required:
raise ValueError(f"Missing required parameters: {missing_required}")
# Type validation would go here
# For production, use jsonschema library for comprehensive validation
def _sanitize_parameters(self, kwargs: Dict[str, Any]) -> Dict[str, Any]:
"""Sanitize parameters for logging (remove sensitive data)."""
# Remove or mask sensitive parameters
sanitized = kwargs.copy()
sensitive_keys = ['password', 'token', 'secret', 'key']
for key in sanitized:
if any(sensitive in key.lower() for sensitive in sensitive_keys):
sanitized[key] = "***MASKED***"
return sanitized
def get_statistics(self) -> Dict[str, Any]:
"""Get tool usage statistics."""
return {
'name': self.name,
'category': self.category.value,
'call_count': self.call_count,
'total_execution_time_ms': self.total_execution_time,
'average_execution_time_ms': (
self.total_execution_time / self.call_count
if self.call_count > 0 else 0
)
}
class DatabaseTool(BaseTool):
"""Base class for database-related tools."""
def __init__(self, name: str, description: str, db_provider):
super().__init__(name, description, ToolCategory.DATABASE_QUERY)
self.db_provider = db_provider
@asynccontextmanager
async def get_connection(self):
"""Get database connection with proper context management."""
conn = None
try:
conn = await self.db_provider.get_connection()
yield conn
finally:
if conn:
await self.db_provider.release_connection(conn)
async def execute_query(
self,
query: str,
params: tuple = None,
store_id: str = None
) -> ToolResult:
"""Execute database query with security and performance monitoring."""
async with self.get_connection() as conn:
try:
# Set store context if provided
if store_id:
await conn.execute("SELECT retail.set_store_context($1)", store_id)
# Execute query
start_time = time.time()
if params:
rows = await conn.fetch(query, *params)
else:
rows = await conn.fetch(query)
execution_time = (time.time() - start_time) * 1000
# Convert rows to dictionaries
data = [dict(row) for row in rows]
return ToolResult(
success=True,
data=data,
row_count=len(data),
execution_time_ms=execution_time
)
except Exception as e:
logger.error(f"Database query failed: {str(e)}")
return ToolResult(
success=False,
error=f"Query execution failed: {str(e)}"
)# mcp_server/tools/query_validator.py
"""
SQL query validation and security for MCP tools.
"""
import re
import sqlparse
from typing import List, Dict, Any, Set
from enum import Enum
class QueryRisk(Enum):
"""Query risk levels."""
LOW = "low"
MEDIUM = "medium"
HIGH = "high"
CRITICAL = "critical"
class QueryValidator:
"""Validate and analyze SQL queries for security risks."""
# Dangerous SQL keywords and patterns
DANGEROUS_KEYWORDS = {
'DROP', 'DELETE', 'TRUNCATE', 'ALTER', 'CREATE', 'INSERT',
'UPDATE', 'GRANT', 'REVOKE', 'EXEC', 'EXECUTE', 'sp_',
'xp_', 'BULK', 'OPENROWSET', 'OPENDATASOURCE'
}
# Allowed read-only operations
SAFE_KEYWORDS = {
'SELECT', 'WITH', 'UNION', 'ORDER', 'GROUP', 'HAVING',
'WHERE', 'FROM', 'JOIN', 'AS', 'ON', 'IN', 'EXISTS',
'CASE', 'WHEN', 'THEN', 'ELSE', 'END', 'AND', 'OR', 'NOT'
}
# Allowed schemas and tables
ALLOWED_SCHEMAS = {'retail', 'information_schema', 'pg_catalog'}
ALLOWED_TABLES = {
'customers', 'products', 'sales_transactions',
'sales_transaction_items', 'product_categories',
'product_embeddings', 'stores'
}
def __init__(self):
self.injection_patterns = [
# SQL injection patterns
r"(\b(UNION|union)\s+(ALL\s+)?(SELECT|select))",
r"(\b(DROP|drop)\s+(TABLE|table|DATABASE|database))",
r"(\b(DELETE|delete)\s+(FROM|from))",
r"(\b(INSERT|insert)\s+(INTO|into))",
r"(\b(UPDATE|update)\s+\w+\s+(SET|set))",
r"(\b(EXEC|exec|EXECUTE|execute)\s*\()",
r"(\b(sp_|xp_)\w+)",
r"(--\s*$)", # SQL comments
r"(/\*.*?\*/)", # Block comments
r"(;\s*(DROP|DELETE|INSERT|UPDATE|CREATE|ALTER))",
r"(\bOR\b\s+['\"]?\w+['\"]?\s*=\s*['\"]?\w+['\"]?)", # OR injection
r"(\bAND\b\s+['\"]?\w+['\"]?\s*=\s*['\"]?\w+['\"]?)", # AND injection
]
self.compiled_patterns = [re.compile(pattern, re.IGNORECASE) for pattern in self.injection_patterns]
def validate_query(self, query: str) -> Dict[str, Any]:
"""Comprehensive query validation."""
validation_result = {
'is_safe': True,
'risk_level': QueryRisk.LOW,
'issues': [],
'warnings': [],
'allowed_operations': [],
'metadata': {}
}
try:
# Parse the query
parsed = sqlparse.parse(query)
if not parsed:
validation_result['is_safe'] = False
validation_result['issues'].append("Unable to parse query")
validation_result['risk_level'] = QueryRisk.HIGH
return validation_result
# Analyze each statement
for statement in parsed:
self._analyze_statement(statement, validation_result)
# Check for injection patterns
self._check_injection_patterns(query, validation_result)
# Validate table/schema access
self._validate_table_access(query, validation_result)
# Determine final risk level
self._determine_risk_level(validation_result)
except Exception as e:
validation_result['is_safe'] = False
validation_result['issues'].append(f"Query analysis failed: {str(e)}")
validation_result['risk_level'] = QueryRisk.CRITICAL
return validation_result
def _analyze_statement(self, statement, validation_result):
"""Analyze individual SQL statement."""
# Get statement type
stmt_type = statement.get_type()
# Check if statement type is allowed
if stmt_type and stmt_type.upper() not in ['SELECT', 'WITH']:
validation_result['issues'].append(f"Disallowed statement type: {stmt_type}")
validation_result['is_safe'] = False
return
# Extract tokens and analyze
for token in statement.flatten():
if token.ttype is sqlparse.tokens.Keyword:
keyword = token.value.upper()
if keyword in self.DANGEROUS_KEYWORDS:
validation_result['issues'].append(f"Dangerous keyword detected: {keyword}")
validation_result['is_safe'] = False
elif keyword in self.SAFE_KEYWORDS:
if keyword not in validation_result['allowed_operations']:
validation_result['allowed_operations'].append(keyword)
def _check_injection_patterns(self, query: str, validation_result):
"""Check for SQL injection patterns."""
for pattern in self.compiled_patterns:
matches = pattern.findall(query)
if matches:
validation_result['issues'].append(f"Potential injection pattern detected")
validation_result['is_safe'] = False
def _validate_table_access(self, query: str, validation_result):
"""Validate that only allowed tables/schemas are accessed."""
# Extract table names (simplified approach)
# In production, use proper SQL parsing
from_match = re.findall(r'FROM\s+(\w+\.?\w*)', query, re.IGNORECASE)
join_match = re.findall(r'JOIN\s+(\w+\.?\w*)', query, re.IGNORECASE)
all_tables = from_match + join_match
for table_ref in all_tables:
if '.' in table_ref:
schema, table = table_ref.split('.', 1)
if schema.lower() not in self.ALLOWED_SCHEMAS:
validation_result['issues'].append(f"Access to unauthorized schema: {schema}")
validation_result['is_safe'] = False
if table.lower() not in self.ALLOWED_TABLES:
validation_result['warnings'].append(f"Access to table: {table}")
else:
# Assume retail schema if not specified
if table_ref.lower() not in self.ALLOWED_TABLES:
validation_result['warnings'].append(f"Access to table: {table_ref}")
def _determine_risk_level(self, validation_result):
"""Determine overall risk level."""
if not validation_result['is_safe']:
if any('injection' in issue.lower() for issue in validation_result['issues']):
validation_result['risk_level'] = QueryRisk.CRITICAL
elif any('DROP' in issue or 'DELETE' in issue for issue in validation_result['issues']):
validation_result['risk_level'] = QueryRisk.HIGH
else:
validation_result['risk_level'] = QueryRisk.MEDIUM
elif validation_result['warnings']:
validation_result['risk_level'] = QueryRisk.LOW
else:
validation_result['risk_level'] = QueryRisk.LOW
# Global validator instance
query_validator = QueryValidator()# mcp_server/tools/sales_analysis.py
"""
Comprehensive sales analysis tool for retail data querying.
"""
from typing import Dict, Any, List, Optional
from datetime import datetime, timedelta
from .base import DatabaseTool, ToolResult
from .query_validator import query_validator
class SalesAnalysisTool(DatabaseTool):
"""Advanced sales analysis and reporting tool."""
def __init__(self, db_provider):
super().__init__(
name="execute_sales_query",
description="Execute sophisticated sales analysis queries with natural language support",
db_provider=db_provider
)
# Pre-built query templates for common analysis
self.query_templates = {
'daily_sales': """
SELECT
DATE(transaction_date) as sales_date,
COUNT(*) as transaction_count,
SUM(total_amount) as total_revenue,
AVG(total_amount) as avg_transaction_value,
COUNT(DISTINCT customer_id) as unique_customers
FROM retail.sales_transactions
WHERE transaction_date >= $1 AND transaction_date <= $2
AND transaction_type = 'sale'
GROUP BY DATE(transaction_date)
ORDER BY sales_date DESC
""",
'top_products': """
SELECT
p.product_name,
p.brand,
SUM(sti.quantity) as total_quantity_sold,
SUM(sti.total_price) as total_revenue,
COUNT(DISTINCT st.transaction_id) as transaction_count,
AVG(sti.unit_price) as avg_price
FROM retail.sales_transaction_items sti
JOIN retail.sales_transactions st ON sti.transaction_id = st.transaction_id
JOIN retail.products p ON sti.product_id = p.product_id
WHERE st.transaction_date >= $1 AND st.transaction_date <= $2
AND st.transaction_type = 'sale'
GROUP BY p.product_id, p.product_name, p.brand
ORDER BY total_revenue DESC
LIMIT $3
""",
'customer_analysis': """
SELECT
c.customer_id,
c.first_name || ' ' || c.last_name as customer_name,
c.loyalty_tier,
COUNT(st.transaction_id) as transaction_count,
SUM(st.total_amount) as total_spent,
AVG(st.total_amount) as avg_transaction_value,
MAX(st.transaction_date) as last_purchase_date,
DATE_PART('day', CURRENT_DATE - MAX(st.transaction_date)) as days_since_last_purchase
FROM retail.customers c
LEFT JOIN retail.sales_transactions st ON c.customer_id = st.customer_id
WHERE st.transaction_date >= $1 AND st.transaction_date <= $2
AND st.transaction_type = 'sale'
GROUP BY c.customer_id, c.first_name, c.last_name, c.loyalty_tier
HAVING COUNT(st.transaction_id) > 0
ORDER BY total_spent DESC
LIMIT $3
""",
'category_performance': """
SELECT
pc.category_name,
COUNT(DISTINCT p.product_id) as unique_products,
SUM(sti.quantity) as total_quantity_sold,
SUM(sti.total_price) as total_revenue,
AVG(sti.unit_price) as avg_price,
COUNT(DISTINCT st.transaction_id) as transaction_count
FROM retail.product_categories pc
JOIN retail.products p ON pc.category_id = p.category_id
JOIN retail.sales_transaction_items sti ON p.product_id = sti.product_id
JOIN retail.sales_transactions st ON sti.transaction_id = st.transaction_id
WHERE st.transaction_date >= $1 AND st.transaction_date <= $2
AND st.transaction_type = 'sale'
GROUP BY pc.category_id, pc.category_name
ORDER BY total_revenue DESC
""",
'sales_trends': """
WITH daily_sales AS (
SELECT
DATE(transaction_date) as sales_date,
SUM(total_amount) as daily_revenue,
COUNT(*) as daily_transactions
FROM retail.sales_transactions
WHERE transaction_date >= $1 AND transaction_date <= $2
AND transaction_type = 'sale'
GROUP BY DATE(transaction_date)
),
trend_analysis AS (
SELECT
sales_date,
daily_revenue,
daily_transactions,
LAG(daily_revenue, 1) OVER (ORDER BY sales_date) as prev_day_revenue,
LAG(daily_revenue, 7) OVER (ORDER BY sales_date) as prev_week_revenue,
AVG(daily_revenue) OVER (
ORDER BY sales_date
ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
) as rolling_7day_avg
FROM daily_sales
)
SELECT
sales_date,
daily_revenue,
daily_transactions,
rolling_7day_avg,
CASE
WHEN prev_day_revenue IS NOT NULL THEN
ROUND(((daily_revenue - prev_day_revenue) / prev_day_revenue * 100)::numeric, 2)
ELSE NULL
END as day_over_day_growth_pct,
CASE
WHEN prev_week_revenue IS NOT NULL THEN
ROUND(((daily_revenue - prev_week_revenue) / prev_week_revenue * 100)::numeric, 2)
ELSE NULL
END as week_over_week_growth_pct
FROM trend_analysis
ORDER BY sales_date DESC
"""
}
async def execute(self, **kwargs) -> ToolResult:
"""Execute sales analysis query."""
query_type = kwargs.get('query_type', 'custom')
store_id = kwargs.get('store_id')
if not store_id:
return ToolResult(
success=False,
error="store_id is required for sales analysis"
)
try:
if query_type in self.query_templates:
return await self._execute_template_query(query_type, kwargs)
elif query_type == 'custom':
return await self._execute_custom_query(kwargs)
else:
return ToolResult(
success=False,
error=f"Unknown query type: {query_type}"
)
except Exception as e:
return ToolResult(
success=False,
error=f"Sales analysis failed: {str(e)}"
)
async def _execute_template_query(self, query_type: str, kwargs: Dict[str, Any]) -> ToolResult:
"""Execute pre-built template query."""
query = self.query_templates[query_type]
store_id = kwargs['store_id']
# Default parameters for template queries
start_date = kwargs.get('start_date', (datetime.now() - timedelta(days=30)).date())
end_date = kwargs.get('end_date', datetime.now().date())
limit = kwargs.get('limit', 20)
# Convert string dates if needed
if isinstance(start_date, str):
start_date = datetime.fromisoformat(start_date).date()
if isinstance(end_date, str):
end_date = datetime.fromisoformat(end_date).date()
# Execute query with parameters
params = (start_date, end_date, limit) if '$3' in query else (start_date, end_date)
result = await self.execute_query(query, params, store_id)
if result.success:
result.metadata = {
'query_type': query_type,
'date_range': f"{start_date} to {end_date}",
'store_id': store_id,
'analysis_type': 'template'
}
return result
async def _execute_custom_query(self, kwargs: Dict[str, Any]) -> ToolResult:
"""Execute custom SQL query with validation."""
custom_query = kwargs.get('query')
store_id = kwargs['store_id']
if not custom_query:
return ToolResult(
success=False,
error="Custom query is required when query_type is 'custom'"
)
# Validate the query for security
validation = query_validator.validate_query(custom_query)
if not validation['is_safe']:
return ToolResult(
success=False,
error=f"Query validation failed: {', '.join(validation['issues'])}",
metadata={
'validation_result': validation,
'risk_level': validation['risk_level'].value
}
)
# Execute validated query
result = await self.execute_query(custom_query, None, store_id)
if result.success:
result.metadata = {
'query_type': 'custom',
'store_id': store_id,
'validation_warnings': validation.get('warnings', []),
'analysis_type': 'custom'
}
return result
def get_input_schema(self) -> Dict[str, Any]:
"""Get input schema for the sales analysis tool."""
return {
"type": "object",
"properties": {
"query_type": {
"type": "string",
"enum": list(self.query_templates.keys()) + ["custom"],
"description": "Type of sales analysis to perform",
"default": "daily_sales"
},
"store_id": {
"type": "string",
"description": "Store ID for data isolation",
"pattern": "^[a-zA-Z0-9_-]+$"
},
"start_date": {
"type": "string",
"format": "date",
"description": "Start date for analysis (YYYY-MM-DD)"
},
"end_date": {
"type": "string",
"format": "date",
"description": "End date for analysis (YYYY-MM-DD)"
},
"limit": {
"type": "integer",
"minimum": 1,
"maximum": 1000,
"description": "Maximum number of results to return",
"default": 20
},
"query": {
"type": "string",
"description": "Custom SQL query (required when query_type is 'custom')"
}
},
"required": ["store_id"],
"additionalProperties": False
}# mcp_server/tools/schema_introspection.py
"""
Database schema introspection and metadata tools.
"""
from typing import Dict, Any, List
from .base import DatabaseTool, ToolResult, ToolCategory
class SchemaIntrospectionTool(DatabaseTool):
"""Tool for exploring database schema and metadata."""
def __init__(self, db_provider):
super().__init__(
name="get_table_schema",
description="Get detailed schema information for database tables",
db_provider=db_provider
)
self.category = ToolCategory.SCHEMA_INTROSPECTION
async def execute(self, **kwargs) -> ToolResult:
"""Execute schema introspection."""
table_name = kwargs.get('table_name')
include_constraints = kwargs.get('include_constraints', True)
include_indexes = kwargs.get('include_indexes', True)
include_statistics = kwargs.get('include_statistics', False)
try:
if table_name:
return await self._get_single_table_schema(
table_name, include_constraints, include_indexes, include_statistics
)
else:
return await self._get_all_tables_schema(include_constraints, include_indexes)
except Exception as e:
return ToolResult(
success=False,
error=f"Schema introspection failed: {str(e)}"
)
async def _get_single_table_schema(
self,
table_name: str,
include_constraints: bool,
include_indexes: bool,
include_statistics: bool
) -> ToolResult:
"""Get detailed schema for a single table."""
schema_info = {
'table_name': table_name,
'columns': [],
'constraints': [],
'indexes': [],
'statistics': {}
}
async with self.get_connection() as conn:
# Get column information
columns_query = """
SELECT
column_name,
data_type,
is_nullable,
column_default,
character_maximum_length,
numeric_precision,
numeric_scale,
ordinal_position,
udt_name
FROM information_schema.columns
WHERE table_schema = 'retail' AND table_name = $1
ORDER BY ordinal_position
"""
columns = await conn.fetch(columns_query, table_name)
schema_info['columns'] = [dict(col) for col in columns]
# Get constraints if requested
if include_constraints:
constraints_query = """
SELECT
constraint_name,
constraint_type,
column_name,
foreign_table_name,
foreign_column_name
FROM information_schema.table_constraints tc
LEFT JOIN information_schema.key_column_usage kcu
ON tc.constraint_name = kcu.constraint_name
LEFT JOIN information_schema.referential_constraints rc
ON tc.constraint_name = rc.constraint_name
LEFT JOIN information_schema.key_column_usage fkcu
ON rc.unique_constraint_name = fkcu.constraint_name
WHERE tc.table_schema = 'retail' AND tc.table_name = $1
"""
constraints = await conn.fetch(constraints_query, table_name)
schema_info['constraints'] = [dict(const) for const in constraints]
# Get indexes if requested
if include_indexes:
indexes_query = """
SELECT
indexname as index_name,
indexdef as index_definition,
tablespace
FROM pg_indexes
WHERE schemaname = 'retail' AND tablename = $1
"""
indexes = await conn.fetch(indexes_query, table_name)
schema_info['indexes'] = [dict(idx) for idx in indexes]
# Get table statistics if requested
if include_statistics:
stats_query = """
SELECT
n_tup_ins as inserts,
n_tup_upd as updates,
n_tup_del as deletes,
n_live_tup as live_tuples,
n_dead_tup as dead_tuples,
last_vacuum,
last_autovacuum,
last_analyze,
last_autoanalyze
FROM pg_stat_user_tables
WHERE schemaname = 'retail' AND relname = $1
"""
stats = await conn.fetchrow(stats_query, table_name)
if stats:
schema_info['statistics'] = dict(stats)
return ToolResult(
success=True,
data=schema_info,
metadata={
'table_name': table_name,
'schema': 'retail',
'introspection_type': 'single_table'
}
)
async def _get_all_tables_schema(
self,
include_constraints: bool,
include_indexes: bool
) -> ToolResult:
"""Get schema information for all tables."""
async with self.get_connection() as conn:
# Get all tables in retail schema
tables_query = """
SELECT
table_name,
table_type
FROM information_schema.tables
WHERE table_schema = 'retail'
ORDER BY table_name
"""
tables = await conn.fetch(tables_query)
schema_info = {
'schema_name': 'retail',
'tables': []
}
for table in tables:
table_info = {
'table_name': table['table_name'],
'table_type': table['table_type'],
'columns': []
}
# Get columns for each table
columns_query = """
SELECT
column_name,
data_type,
is_nullable,
column_default
FROM information_schema.columns
WHERE table_schema = 'retail' AND table_name = $1
ORDER BY ordinal_position
"""
columns = await conn.fetch(columns_query, table['table_name'])
table_info['columns'] = [dict(col) for col in columns]
schema_info['tables'].append(table_info)
return ToolResult(
success=True,
data=schema_info,
metadata={
'schema': 'retail',
'table_count': len(schema_info['tables']),
'introspection_type': 'all_tables'
}
)
def get_input_schema(self) -> Dict[str, Any]:
"""Get input schema for schema introspection tool."""
return {
"type": "object",
"properties": {
"table_name": {
"type": "string",
"description": "Specific table name to introspect (optional - if not provided, all tables are returned)",
"pattern": "^[a-zA-Z_][a-zA-Z0-9_]*$"
},
"include_constraints": {
"type": "boolean",
"description": "Include constraint information",
"default": True
},
"include_indexes": {
"type": "boolean",
"description": "Include index information",
"default": True
},
"include_statistics": {
"type": "boolean",
"description": "Include table statistics",
"default": False
}
},
"additionalProperties": False
}
class MultiTableSchemaTool(DatabaseTool):
"""Tool for getting schema information for multiple tables at once."""
def __init__(self, db_provider):
super().__init__(
name="get_multiple_table_schemas",
description="Get schema information for multiple tables efficiently",
db_provider=db_provider
)
self.category = ToolCategory.SCHEMA_INTROSPECTION
async def execute(self, **kwargs) -> ToolResult:
"""Execute multi-table schema introspection."""
table_names = kwargs.get('table_names', [])
if not table_names:
return ToolResult(
success=False,
error="At least one table name is required"
)
try:
schemas = {}
async with self.get_connection() as conn:
for table_name in table_names:
# Get table schema
schema_query = """
SELECT
c.column_name,
c.data_type,
c.is_nullable,
c.column_default,
c.character_maximum_length,
tc.constraint_type,
kcu.constraint_name
FROM information_schema.columns c
LEFT JOIN information_schema.key_column_usage kcu
ON c.table_name = kcu.table_name
AND c.column_name = kcu.column_name
AND c.table_schema = kcu.table_schema
LEFT JOIN information_schema.table_constraints tc
ON kcu.constraint_name = tc.constraint_name
AND kcu.table_schema = tc.table_schema
WHERE c.table_schema = 'retail' AND c.table_name = $1
ORDER BY c.ordinal_position
"""
columns = await conn.fetch(schema_query, table_name)
if columns:
schemas[table_name] = {
'table_name': table_name,
'columns': [dict(col) for col in columns]
}
else:
schemas[table_name] = {
'table_name': table_name,
'error': 'Table not found or not accessible'
}
return ToolResult(
success=True,
data=schemas,
metadata={
'requested_tables': table_names,
'found_tables': [name for name, info in schemas.items() if 'error' not in info],
'missing_tables': [name for name, info in schemas.items() if 'error' in info]
}
)
except Exception as e:
return ToolResult(
success=False,
error=f"Multi-table schema introspection failed: {str(e)}"
)
def get_input_schema(self) -> Dict[str, Any]:
"""Get input schema for multi-table schema tool."""
return {
"type": "object",
"properties": {
"table_names": {
"type": "array",
"items": {
"type": "string",
"pattern": "^[a-zA-Z_][a-zA-Z0-9_]*$"
},
"description": "List of table names to get schema information for",
"minItems": 1,
"maxItems": 20
}
},
"required": ["table_names"],
"additionalProperties": False
}# mcp_server/tools/business_intelligence.py
"""
Advanced business intelligence and analytics tools.
"""
from typing import Dict, Any, List
from datetime import datetime, timedelta
from .base import DatabaseTool, ToolResult, ToolCategory
class BusinessIntelligenceTool(DatabaseTool):
"""Advanced analytics tool for business intelligence queries."""
def __init__(self, db_provider):
super().__init__(
name="generate_business_insights",
description="Generate comprehensive business intelligence reports and insights",
db_provider=db_provider
)
self.category = ToolCategory.ANALYTICS
async def execute(self, **kwargs) -> ToolResult:
"""Execute business intelligence analysis."""
analysis_type = kwargs.get('analysis_type', 'summary')
store_id = kwargs.get('store_id')
if not store_id:
return ToolResult(
success=False,
error="store_id is required for business intelligence analysis"
)
try:
if analysis_type == 'summary':
return await self._generate_business_summary(kwargs)
elif analysis_type == 'customer_segmentation':
return await self._analyze_customer_segmentation(kwargs)
elif analysis_type == 'product_performance':
return await self._analyze_product_performance(kwargs)
elif analysis_type == 'seasonal_trends':
return await self._analyze_seasonal_trends(kwargs)
else:
return ToolResult(
success=False,
error=f"Unknown analysis type: {analysis_type}"
)
except Exception as e:
return ToolResult(
success=False,
error=f"Business intelligence analysis failed: {str(e)}"
)
async def _generate_business_summary(self, kwargs: Dict[str, Any]) -> ToolResult:
"""Generate comprehensive business summary."""
store_id = kwargs['store_id']
days = kwargs.get('days', 30)
summary_query = """
WITH date_range AS (
SELECT CURRENT_DATE - INTERVAL '%s days' as start_date,
CURRENT_DATE as end_date
),
sales_summary AS (
SELECT
COUNT(*) as total_transactions,
COUNT(DISTINCT customer_id) as unique_customers,
SUM(total_amount) as total_revenue,
AVG(total_amount) as avg_transaction_value,
COUNT(DISTINCT DATE(transaction_date)) as active_days
FROM retail.sales_transactions st, date_range dr
WHERE st.transaction_date >= dr.start_date
AND st.transaction_date <= dr.end_date
AND st.transaction_type = 'sale'
),
product_summary AS (
SELECT
COUNT(DISTINCT p.product_id) as products_sold,
SUM(sti.quantity) as total_items_sold
FROM retail.sales_transaction_items sti
JOIN retail.sales_transactions st ON sti.transaction_id = st.transaction_id
JOIN retail.products p ON sti.product_id = p.product_id
CROSS JOIN date_range dr
WHERE st.transaction_date >= dr.start_date
AND st.transaction_date <= dr.end_date
AND st.transaction_type = 'sale'
),
top_category AS (
SELECT
pc.category_name,
SUM(sti.total_price) as category_revenue
FROM retail.product_categories pc
JOIN retail.products p ON pc.category_id = p.category_id
JOIN retail.sales_transaction_items sti ON p.product_id = sti.product_id
JOIN retail.sales_transactions st ON sti.transaction_id = st.transaction_id
CROSS JOIN date_range dr
WHERE st.transaction_date >= dr.start_date
AND st.transaction_date <= dr.end_date
AND st.transaction_type = 'sale'
GROUP BY pc.category_name
ORDER BY category_revenue DESC
LIMIT 1
)
SELECT
ss.*,
ps.products_sold,
ps.total_items_sold,
tc.category_name as top_category,
tc.category_revenue as top_category_revenue,
CASE
WHEN ss.active_days > 0 THEN ss.total_revenue / ss.active_days
ELSE 0
END as avg_daily_revenue
FROM sales_summary ss
CROSS JOIN product_summary ps
CROSS JOIN top_category tc
""" % days
result = await self.execute_query(summary_query, None, store_id)
if result.success and result.data:
summary = result.data[0]
# Add derived insights
insights = {
'revenue_trend': 'stable', # Would calculate based on historical data
'customer_retention': f"{summary.get('unique_customers', 0)} active customers",
'performance_indicators': {
'transactions_per_day': round(summary.get('total_transactions', 0) / max(summary.get('active_days', 1), 1), 2),
'revenue_per_customer': round(summary.get('total_revenue', 0) / max(summary.get('unique_customers', 1), 1), 2),
'items_per_transaction': round(summary.get('total_items_sold', 0) / max(summary.get('total_transactions', 1), 1), 2)
}
}
summary['insights'] = insights
result.data = [summary]
result.metadata = {
'analysis_type': 'business_summary',
'period_days': days,
'store_id': store_id
}
return result
def get_input_schema(self) -> Dict[str, Any]:
"""Get input schema for business intelligence tool."""
return {
"type": "object",
"properties": {
"analysis_type": {
"type": "string",
"enum": ["summary", "customer_segmentation", "product_performance", "seasonal_trends"],
"description": "Type of business intelligence analysis to perform",
"default": "summary"
},
"store_id": {
"type": "string",
"description": "Store ID for analysis",
"pattern": "^[a-zA-Z0-9_-]+$"
},
"days": {
"type": "integer",
"minimum": 1,
"maximum": 365,
"description": "Number of days to analyze",
"default": 30
}
},
"required": ["store_id"],
"additionalProperties": False
}
class UtilityTool(DatabaseTool):
"""Utility tool for common operations."""
def __init__(self, db_provider):
super().__init__(
name="get_current_utc_date",
description="Get current UTC date and time for reference",
db_provider=db_provider
)
self.category = ToolCategory.UTILITY
async def execute(self, **kwargs) -> ToolResult:
"""Execute utility operation."""
format_type = kwargs.get('format', 'iso')
try:
async with self.get_connection() as conn:
if format_type == 'iso':
query = "SELECT CURRENT_TIMESTAMP AT TIME ZONE 'UTC' as current_utc_datetime"
elif format_type == 'epoch':
query = "SELECT EXTRACT(EPOCH FROM CURRENT_TIMESTAMP AT TIME ZONE 'UTC') as current_utc_epoch"
elif format_type == 'date_only':
query = "SELECT CURRENT_DATE as current_date"
else:
return ToolResult(
success=False,
error=f"Unknown format type: {format_type}"
)
result = await conn.fetchrow(query)
return ToolResult(
success=True,
data=dict(result),
metadata={
'format_type': format_type,
'timezone': 'UTC'
}
)
except Exception as e:
return ToolResult(
success=False,
error=f"Utility operation failed: {str(e)}"
)
def get_input_schema(self) -> Dict[str, Any]:
"""Get input schema for utility tool."""
return {
"type": "object",
"properties": {
"format": {
"type": "string",
"enum": ["iso", "epoch", "date_only"],
"description": "Format for the returned date/time",
"default": "iso"
}
},
"additionalProperties": False
}完成本實驗後，您應該具備以下能力：
✅ 高級工具架構：實現具有全面錯誤處理的高級 MCP 工具
✅ 查詢驗證：構建安全的 SQL 驗證以防止注入攻擊
✅ 資料庫工具：創建強大的銷售分析和結構檢視功能
✅ 商業智能：開發分析工具以獲得全面的商業洞察
✅ 性能優化：應用快取、連接池和查詢優化技術
✅ 安全集成：實現基於角色的訪問控制和審計記錄
繼續進行 實驗 07：語義搜索集成，以：
- 集成 MCP 工具的向量搜索功能
- 構建語義產品搜索功能
- 實現 AI 驅動的查詢理解
- 創建結合傳統與向量查詢的混合搜索
- Model Context Protocol 文件 - 官方 MCP 規範
- FastMCP 框架 - Python MCP 實現
- MCP 工具模式 - 工具實現範例
- SQL 注入防護 - OWASP 安全指南
- PostgreSQL 安全性 - 資料庫安全最佳實踐
- 查詢驗證技術 - 安全查詢模式
- 資料庫查詢優化 - PostgreSQL 性能指南
- 連接池最佳實踐 - 連接管理
- Async Python 模式 - 非同步程式設計指南
上一章: 實驗 05：MCP 伺服器實現
下一章: 實驗 07：語義搜索集成
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