This chapter explores the implementation of Model Context Protocol (MCP) for mainframe modernization, providing a standardized framework for AI agents to interact with mainframe systems, code, and development tools. MCP enables sophisticated multi-agent orchestration for complex modernization projects.
- 15.1 Introduction to Model Context Protocol
- 15.2 MCP Server Implementation
- 15.3 Multi-Agent Orchestration
- 15.4 Production MCP Deployment
- 15.5 Integration with Azure AI Platform
- 15.6 Best Practices and Patterns
Model Context Protocol (MCP) is an open protocol that enables seamless integration between AI models and external systems. For mainframe modernization, MCP provides:
- Standardized Communication: Unified interface for agents to interact with mainframe systems
- Context Management: Efficient handling of mainframe-specific contexts (COBOL, Natural, JCL)
- Tool Integration: Seamless connection with development tools and CI/CD pipelines
- Security: Built-in authentication and authorization mechanisms
| Aspect | Traditional Integration | MCP-Based Integration |
|---|---|---|
| Protocol | Custom APIs, varied formats | Standardized protocol |
| Context Handling | Manual context management | Automatic context propagation |
| Scalability | Limited by custom implementations | Built for distributed systems |
| Tool Integration | Point-to-point integrations | Unified tool registry |
| Security | Varies by implementation | Standardized security model |
- Language-Specific Contexts: Native support for COBOL, Natural, PL/I, and Assembler contexts
- Legacy System Integration: Seamless connection with existing mainframe tools
- Parallel Processing: Multi-agent orchestration for large-scale transformations
- Knowledge Preservation: Capture and reuse mainframe expertise
- Incremental Modernization: Support for phased migration approaches
- Compliance and Audit: Built-in tracking and compliance features
- Performance Optimization: Efficient resource utilization
graph TB
subgraph "AI Agents"
A1[Discovery Agent]
A2[Analysis Agent]
A3[Transformation Agent]
A4[Testing Agent]
A5[Deployment Agent]
end
subgraph "MCP Server"
MCP[MCP Core]
CTX[Context Manager]
TOOLS[Tool Registry]
SEC[Security Layer]
end
subgraph "Mainframe Systems"
COBOL[COBOL Apps]
NAT[Natural/Adabas]
JCL[JCL Scripts]
CICS[CICS]
end
subgraph "Modern Infrastructure"
GH[GitHub]
AZ[Azure AI Platform]
K8S[Kubernetes]
end
A1 & A2 & A3 & A4 & A5 <--> MCP
MCP <--> COBOL & NAT & JCL & CICS
MCP <--> GH & AZ & K8S
# mcp_server/mainframe_mcp_server.py
import asyncio
from typing import Dict, List, Any, Optional
from dataclasses import dataclass
import json
import logging
from abc import ABC, abstractmethod
@dataclass
class MCPConfig:
"""Configuration for Mainframe MCP Server"""
server_name: str
port: int
contexts: List[str]
tools: List[str]
security_enabled: bool = True
max_concurrent_agents: int = 50
timeout_seconds: int = 300
class MainframeMCPServer:
"""
MCP Server implementation for mainframe modernization
Handles agent connections and context management
"""
def __init__(self, config: MCPConfig):
self.config = config
self.contexts: Dict[str, 'BaseContext'] = {}
self.tools: Dict[str, 'BaseTool'] = {}
self.agents: Dict[str, 'Agent'] = {}
self.logger = logging.getLogger(__name__)
async def start(self):
"""Start the MCP server"""
self.logger.info(f"Starting MCP Server on port {self.config.port}")
# Initialize contexts
await self._initialize_contexts()
# Register tools
await self._register_tools()
# Start server
server = await asyncio.start_server(
self._handle_client,
'0.0.0.0',
self.config.port
)
async with server:
await server.serve_forever()
async def _initialize_contexts(self):
"""Initialize language-specific contexts"""
for context_name in self.config.contexts:
if context_name == 'cobol':
self.contexts['cobol'] = COBOLContext()
elif context_name == 'natural':
self.contexts['natural'] = NaturalContext()
elif context_name == 'adabas':
self.contexts['adabas'] = AdabasContext()
elif context_name == 'jcl':
self.contexts['jcl'] = JCLContext()
elif context_name == 'pl1':
self.contexts['pl1'] = PL1Context()
async def _register_tools(self):
"""Register available tools"""
self.tools = {
'analyze_complexity': ComplexityAnalyzer(),
'extract_business_logic': BusinessLogicExtractor(),
'transform_code': CodeTransformer(),
'generate_tests': TestGenerator(),
'map_dependencies': DependencyMapper(),
'validate_transformation': TransformationValidator()
}
async def _handle_client(self, reader, writer):
"""Handle client connections"""
addr = writer.get_extra_info('peername')
self.logger.info(f"New connection from {addr}")
try:
while True:
data = await reader.read(8192)
if not data:
break
message = json.loads(data.decode())
response = await self._process_message(message)
writer.write(json.dumps(response).encode())
await writer.drain()
except Exception as e:
self.logger.error(f"Error handling client: {e}")
finally:
writer.close()
await writer.wait_closed()
async def _process_message(self, message: Dict[str, Any]) -> Dict[str, Any]:
"""Process incoming MCP messages"""
msg_type = message.get('type')
if msg_type == 'register_agent':
return await self._register_agent(message)
elif msg_type == 'execute_tool':
return await self._execute_tool(message)
elif msg_type == 'get_context':
return await self._get_context(message)
elif msg_type == 'update_context':
return await self._update_context(message)
else:
return {'error': f'Unknown message type: {msg_type}'}# mcp_server/contexts/base_context.py
class BaseContext(ABC):
"""Base class for language-specific contexts"""
@abstractmethod
async def analyze(self, code: str) -> Dict[str, Any]:
"""Analyze code in the specific language"""
pass
@abstractmethod
async def get_patterns(self) -> List[Dict[str, Any]]:
"""Get language-specific patterns"""
pass
@abstractmethod
async def validate(self, code: str) -> Dict[str, Any]:
"""Validate code syntax and structure"""
pass
# mcp_server/contexts/cobol_context.py
import re
from typing import Dict, Any, List
class COBOLContext(BaseContext):
"""COBOL-specific context implementation"""
def __init__(self):
self.patterns = self._load_cobol_patterns()
self.keywords = self._load_cobol_keywords()
self.complexity_rules = self._load_complexity_rules()
async def analyze(self, code: str) -> Dict[str, Any]:
"""Analyze COBOL code"""
return {
'language': 'COBOL',
'divisions': self._extract_divisions(code),
'procedures': self._extract_procedures(code),
'data_structures': self._extract_data_structures(code),
'complexity': self._calculate_complexity(code),
'dependencies': self._extract_dependencies(code),
'business_rules': self._extract_business_rules(code)
}
def _extract_divisions(self, code: str) -> List[Dict[str, Any]]:
"""Extract COBOL divisions"""
divisions = []
division_pattern = r'(\w+)\s+DIVISION\.'
for match in re.finditer(division_pattern, code, re.IGNORECASE):
divisions.append({
'name': match.group(1),
'start_line': code[:match.start()].count('\n') + 1,
'type': match.group(1).upper()
})
return divisions
def _extract_procedures(self, code: str) -> List[Dict[str, Any]]:
"""Extract COBOL procedures and paragraphs"""
procedures = []
# Extract sections
section_pattern = r'^(\s*)([A-Z0-9\-]+)\s+SECTION\.'
for match in re.finditer(section_pattern, code, re.MULTILINE):
procedures.append({
'name': match.group(2),
'type': 'SECTION',
'line': code[:match.start()].count('\n') + 1
})
# Extract paragraphs
para_pattern = r'^(\s*)([A-Z0-9\-]+)\.\s*$'
for match in re.finditer(para_pattern, code, re.MULTILINE):
if not match.group(2).endswith('DIVISION'):
procedures.append({
'name': match.group(2),
'type': 'PARAGRAPH',
'line': code[:match.start()].count('\n') + 1
})
return procedures
def _calculate_complexity(self, code: str) -> Dict[str, Any]:
"""Calculate COBOL code complexity metrics"""
lines = code.split('\n')
# Count various complexity indicators
perform_count = len(re.findall(r'\bPERFORM\b', code, re.IGNORECASE))
if_count = len(re.findall(r'\bIF\b', code, re.IGNORECASE))
evaluate_count = len(re.findall(r'\bEVALUATE\b', code, re.IGNORECASE))
call_count = len(re.findall(r'\bCALL\b', code, re.IGNORECASE))
# Calculate cyclomatic complexity
cyclomatic = 1 + if_count + evaluate_count + perform_count
return {
'lines_of_code': len(lines),
'cyclomatic_complexity': cyclomatic,
'perform_statements': perform_count,
'conditional_statements': if_count + evaluate_count,
'external_calls': call_count,
'complexity_score': self._calculate_score(cyclomatic, len(lines))
}
def _calculate_score(self, cyclomatic: int, loc: int) -> str:
"""Calculate complexity score"""
if cyclomatic < 10:
return 'LOW'
elif cyclomatic < 20:
return 'MEDIUM'
elif cyclomatic < 50:
return 'HIGH'
else:
return 'VERY_HIGH'
async def get_patterns(self) -> List[Dict[str, Any]]:
"""Get COBOL-specific patterns for transformation"""
return [
{
'pattern': 'PERFORM_UNTIL',
'regex': r'PERFORM\s+(.+?)\s+UNTIL\s+(.+)',
'description': 'COBOL PERFORM UNTIL loop',
'java_equivalent': 'while (!{condition}) { {body} }'
},
{
'pattern': 'EVALUATE',
'regex': r'EVALUATE\s+(.+?)\s+WHEN\s+(.+?)\s+(.+?)\s+END-EVALUATE',
'description': 'COBOL EVALUATE statement',
'java_equivalent': 'switch ({expression}) { case {value}: {action} }'
},
{
'pattern': 'FILE_IO',
'regex': r'(READ|WRITE|REWRITE|DELETE)\s+(\w+)',
'description': 'COBOL file I/O operations',
'java_equivalent': 'fileService.{operation}({filename})'
}
]
async def validate(self, code: str) -> Dict[str, Any]:
"""Validate COBOL syntax"""
errors = []
warnings = []
# Check for required divisions
if 'IDENTIFICATION DIVISION' not in code.upper():
errors.append({
'type': 'MISSING_DIVISION',
'message': 'IDENTIFICATION DIVISION is required',
'severity': 'ERROR'
})
# Check for deprecated features
if 'ALTER' in code.upper():
warnings.append({
'type': 'DEPRECATED_FEATURE',
'message': 'ALTER statement is deprecated',
'severity': 'WARNING'
})
return {
'valid': len(errors) == 0,
'errors': errors,
'warnings': warnings
}
# mcp_server/contexts/natural_context.py
class NaturalContext(BaseContext):
"""Natural/Adabas-specific context implementation"""
def __init__(self):
self.ddm_parser = DDMParser()
self.map_analyzer = MapAnalyzer()
async def analyze(self, code: str) -> Dict[str, Any]:
"""Analyze Natural code"""
return {
'language': 'Natural',
'programs': self._extract_programs(code),
'maps': self._extract_maps(code),
'ddms': self._extract_ddms(code),
'database_access': self._analyze_db_access(code),
'mu_pe_fields': self._analyze_mu_pe(code),
'complexity': self._calculate_natural_complexity(code)
}
def _extract_programs(self, code: str) -> List[Dict[str, Any]]:
"""Extract Natural programs"""
programs = []
# Find DEFINE DATA sections
define_pattern = r'DEFINE\s+DATA\s+(LOCAL|PARAMETER|GLOBAL)'
for match in re.finditer(define_pattern, code, re.IGNORECASE):
programs.append({
'type': match.group(1),
'line': code[:match.start()].count('\n') + 1
})
return programs
def _analyze_db_access(self, code: str) -> List[Dict[str, Any]]:
"""Analyze database access patterns"""
db_operations = []
# Find Natural database operations
operations = ['READ', 'FIND', 'GET', 'STORE', 'UPDATE', 'DELETE']
for op in operations:
pattern = rf'\b{op}\b\s+(\w+)'
for match in re.finditer(pattern, code, re.IGNORECASE):
db_operations.append({
'operation': op,
'file': match.group(1),
'line': code[:match.start()].count('\n') + 1
})
return db_operations
def _analyze_mu_pe(self, code: str) -> List[Dict[str, Any]]:
"""Analyze Multiple Value (MU) and Periodic Group (PE) fields"""
mu_pe_fields = []
# Pattern for MU/PE field definitions
mu_pattern = r'(\w+)\s+\(\s*(\d+:\d+|\*)\s*\)'
for match in re.finditer(mu_pattern, code):
mu_pe_fields.append({
'field': match.group(1),
'dimensions': match.group(2),
'type': 'MU' if ':' in match.group(2) else 'PE'
})
return mu_pe_fields
# mcp_server/contexts/adabas_context.py
class AdabasContext(BaseContext):
"""Adabas DDM-specific context"""
async def analyze(self, ddm_content: str) -> Dict[str, Any]:
"""Analyze Adabas DDM"""
return {
'type': 'DDM',
'fields': self._parse_ddm_fields(ddm_content),
'descriptors': self._extract_descriptors(ddm_content),
'mu_pe_structures': self._analyze_mu_pe_structures(ddm_content),
'relationships': self._extract_relationships(ddm_content)
}
def _parse_ddm_fields(self, ddm: str) -> List[Dict[str, Any]]:
"""Parse DDM field definitions"""
fields = []
# DDM field pattern
field_pattern = r'^\s*\d+\s+(\w+)\s+([A-Z])\s*(\d+(?:\.\d+)?)'
for match in re.finditer(field_pattern, ddm, re.MULTILINE):
fields.append({
'name': match.group(1),
'type': match.group(2),
'length': match.group(3),
'adabas_type': self._map_adabas_type(match.group(2))
})
return fields
def _map_adabas_type(self, adabas_type: str) -> str:
"""Map Adabas types to modern equivalents"""
type_mapping = {
'A': 'String',
'N': 'BigDecimal',
'P': 'BigDecimal',
'B': 'byte[]',
'F': 'Float',
'I': 'Integer',
'L': 'Boolean'
}
return type_mapping.get(adabas_type, 'String')# mcp_server/tools/base_tool.py
class BaseTool(ABC):
"""Base class for MCP tools"""
@abstractmethod
async def execute(self, params: Dict[str, Any]) -> Dict[str, Any]:
"""Execute the tool with given parameters"""
pass
@abstractmethod
def get_schema(self) -> Dict[str, Any]:
"""Get tool parameter schema"""
pass
# mcp_server/tools/complexity_analyzer.py
class ComplexityAnalyzer(BaseTool):
"""Analyze code complexity across languages"""
async def execute(self, params: Dict[str, Any]) -> Dict[str, Any]:
code = params.get('code')
language = params.get('language')
if language == 'COBOL':
analyzer = COBOLComplexityAnalyzer()
elif language == 'Natural':
analyzer = NaturalComplexityAnalyzer()
else:
return {'error': f'Unsupported language: {language}'}
return await analyzer.analyze(code)
def get_schema(self) -> Dict[str, Any]:
return {
'type': 'object',
'properties': {
'code': {'type': 'string', 'description': 'Source code to analyze'},
'language': {'type': 'string', 'enum': ['COBOL', 'Natural', 'PL/I']},
'options': {
'type': 'object',
'properties': {
'include_metrics': {'type': 'boolean'},
'include_suggestions': {'type': 'boolean'}
}
}
},
'required': ['code', 'language']
}
# mcp_server/tools/business_logic_extractor.py
class BusinessLogicExtractor(BaseTool):
"""Extract business logic from mainframe code"""
def __init__(self):
self.ai_client = AzureAIClient()
async def execute(self, params: Dict[str, Any]) -> Dict[str, Any]:
code = params.get('code')
language = params.get('language')
context = params.get('context', {})
# Use AI to extract business logic
prompt = self._build_extraction_prompt(code, language, context)
result = await self.ai_client.analyze(prompt)
return {
'business_rules': result.get('rules', []),
'calculations': result.get('calculations', []),
'validations': result.get('validations', []),
'workflows': result.get('workflows', []),
'data_transformations': result.get('transformations', [])
}
def _build_extraction_prompt(self, code: str, language: str, context: Dict) -> str:
return f"""
Analyze this {language} code and extract business logic:
Context: {json.dumps(context)}
Code:
{code}
Extract:
1. Business rules and conditions
2. Calculations and formulas
3. Data validations
4. Process workflows
5. Data transformations
"""
# mcp_server/tools/code_transformer.py
class CodeTransformer(BaseTool):
"""Transform mainframe code to modern languages"""
async def execute(self, params: Dict[str, Any]) -> Dict[str, Any]:
source_code = params.get('source_code')
source_language = params.get('source_language')
target_language = params.get('target_language')
options = params.get('options', {})
# Select appropriate transformer
transformer = self._get_transformer(source_language, target_language)
# Apply transformation
result = await transformer.transform(
source_code,
preserve_comments=options.get('preserve_comments', True),
optimize=options.get('optimize', True),
generate_tests=options.get('generate_tests', False)
)
return result
def _get_transformer(self, source: str, target: str) -> 'BaseTransformer':
transformers = {
('COBOL', 'Java'): COBOLToJavaTransformer(),
('COBOL', 'C#'): COBOLToCSharpTransformer(),
('Natural', 'Java'): NaturalToJavaTransformer(),
('PL/I', 'Java'): PL1ToJavaTransformer()
}
key = (source, target)
if key not in transformers:
raise ValueError(f"No transformer for {source} to {target}")
return transformers[key]# mcp_server/orchestration/agent_registry.py
import asyncio
from typing import Dict, List, Optional
from datetime import datetime
import uuid
class AgentRegistry:
"""Registry for managing agents in the MCP ecosystem"""
def __init__(self):
self.agents: Dict[str, 'RegisteredAgent'] = {}
self.capabilities: Dict[str, List[str]] = {}
self._lock = asyncio.Lock()
async def register_agent(self, agent_info: Dict[str, Any]) -> str:
"""Register a new agent"""
async with self._lock:
agent_id = str(uuid.uuid4())
agent = RegisteredAgent(
id=agent_id,
name=agent_info['name'],
type=agent_info['type'],
capabilities=agent_info['capabilities'],
status='active',
registered_at=datetime.utcnow()
)
self.agents[agent_id] = agent
# Index capabilities
for capability in agent.capabilities:
if capability not in self.capabilities:
self.capabilities[capability] = []
self.capabilities[capability].append(agent_id)
return agent_id
async def discover_agents(self, capability: str) -> List['RegisteredAgent']:
"""Discover agents with specific capability"""
agent_ids = self.capabilities.get(capability, [])
return [self.agents[aid] for aid in agent_ids if self.agents[aid].status == 'active']
async def get_agent_status(self, agent_id: str) -> Optional[Dict[str, Any]]:
"""Get current agent status"""
agent = self.agents.get(agent_id)
if not agent:
return None
return {
'id': agent.id,
'name': agent.name,
'status': agent.status,
'capabilities': agent.capabilities,
'last_heartbeat': agent.last_heartbeat,
'metrics': agent.metrics
}
@dataclass
class RegisteredAgent:
id: str
name: str
type: str
capabilities: List[str]
status: str
registered_at: datetime
last_heartbeat: Optional[datetime] = None
metrics: Dict[str, Any] = None# mcp_server/orchestration/task_distributor.py
from abc import ABC, abstractmethod
import random
from typing import List, Dict, Any, Optional
class TaskDistributionStrategy(ABC):
"""Base class for task distribution strategies"""
@abstractmethod
async def select_agent(self, task: 'Task', agents: List['RegisteredAgent']) -> Optional['RegisteredAgent']:
pass
class LoadBalancedDistribution(TaskDistributionStrategy):
"""Distribute tasks based on agent load"""
async def select_agent(self, task: 'Task', agents: List['RegisteredAgent']) -> Optional['RegisteredAgent']:
if not agents:
return None
# Sort agents by current load
sorted_agents = sorted(
agents,
key=lambda a: a.metrics.get('current_load', 0) if a.metrics else 0
)
# Select agent with lowest load
return sorted_agents[0]
class CapabilityBasedDistribution(TaskDistributionStrategy):
"""Distribute tasks based on agent capabilities and performance"""
async def select_agent(self, task: 'Task', agents: List['RegisteredAgent']) -> Optional['RegisteredAgent']:
if not agents:
return None
# Score agents based on capability match and performance
scored_agents = []
for agent in agents:
score = self._calculate_score(task, agent)
scored_agents.append((score, agent))
# Sort by score (highest first)
scored_agents.sort(key=lambda x: x[0], reverse=True)
return scored_agents[0][1] if scored_agents else None
def _calculate_score(self, task: 'Task', agent: 'RegisteredAgent') -> float:
score = 0.0
# Capability match
required_capabilities = task.required_capabilities
agent_capabilities = set(agent.capabilities)
match_ratio = len(required_capabilities.intersection(agent_capabilities)) / len(required_capabilities)
score += match_ratio * 50
# Performance history
if agent.metrics:
success_rate = agent.metrics.get('success_rate', 0.5)
avg_execution_time = agent.metrics.get('avg_execution_time', 100)
score += success_rate * 30
score += (100 - min(avg_execution_time, 100)) * 0.2
return score
class TaskOrchestrator:
"""Orchestrate task execution across multiple agents"""
def __init__(self, registry: AgentRegistry):
self.registry = registry
self.distribution_strategy = CapabilityBasedDistribution()
self.task_queue: asyncio.Queue = asyncio.Queue()
self.active_tasks: Dict[str, 'Task'] = {}
async def submit_task(self, task: 'Task') -> str:
"""Submit a task for execution"""
task.id = str(uuid.uuid4())
task.status = 'queued'
task.submitted_at = datetime.utcnow()
await self.task_queue.put(task)
return task.id
async def process_tasks(self):
"""Main task processing loop"""
while True:
try:
task = await self.task_queue.get()
# Find capable agents
agents = []
for capability in task.required_capabilities:
capable_agents = await self.registry.discover_agents(capability)
agents.extend(capable_agents)
# Remove duplicates
agents = list(set(agents))
# Select best agent
selected_agent = await self.distribution_strategy.select_agent(task, agents)
if selected_agent:
await self._execute_task(task, selected_agent)
else:
# No suitable agent found, requeue
task.retry_count += 1
if task.retry_count < task.max_retries:
await asyncio.sleep(task.retry_delay)
await self.task_queue.put(task)
else:
task.status = 'failed'
task.error = 'No suitable agent found'
except Exception as e:
logging.error(f"Error processing task: {e}")
async def _execute_task(self, task: 'Task', agent: 'RegisteredAgent'):
"""Execute task on selected agent"""
task.status = 'executing'
task.assigned_agent = agent.id
task.started_at = datetime.utcnow()
self.active_tasks[task.id] = task
try:
# Send task to agent via MCP
result = await self._send_to_agent(agent, task)
task.status = 'completed'
task.result = result
task.completed_at = datetime.utcnow()
except Exception as e:
task.status = 'failed'
task.error = str(e)
finally:
del self.active_tasks[task.id]
@dataclass
class Task:
id: Optional[str] = None
type: str = None
required_capabilities: List[str] = None
payload: Dict[str, Any] = None
priority: int = 5
max_retries: int = 3
retry_count: int = 0
retry_delay: int = 60
status: str = 'pending'
assigned_agent: Optional[str] = None
submitted_at: Optional[datetime] = None
started_at: Optional[datetime] = None
completed_at: Optional[datetime] = None
result: Optional[Dict[str, Any]] = None
error: Optional[str] = None# mcp_server/orchestration/communication.py
class InterAgentCommunication:
"""Handle communication between agents"""
def __init__(self, mcp_server: 'MainframeMCPServer'):
self.mcp_server = mcp_server
self.message_bus = MessageBus()
self.conversation_manager = ConversationManager()
async def send_message(self, from_agent: str, to_agent: str, message: Dict[str, Any]):
"""Send message between agents"""
msg = AgentMessage(
id=str(uuid.uuid4()),
from_agent=from_agent,
to_agent=to_agent,
type=message.get('type'),
payload=message.get('payload'),
timestamp=datetime.utcnow()
)
await self.message_bus.publish(msg)
async def broadcast(self, from_agent: str, message: Dict[str, Any], capability: Optional[str] = None):
"""Broadcast message to multiple agents"""
if capability:
# Send to agents with specific capability
agents = await self.mcp_server.registry.discover_agents(capability)
recipients = [a.id for a in agents]
else:
# Send to all agents
recipients = list(self.mcp_server.agents.keys())
for recipient in recipients:
if recipient != from_agent:
await self.send_message(from_agent, recipient, message)
async def request_collaboration(self, requesting_agent: str, task: Dict[str, Any]) -> List[str]:
"""Request collaboration from other agents"""
collaboration_request = {
'type': 'collaboration_request',
'payload': {
'task': task,
'required_capabilities': task.get('capabilities', []),
'deadline': task.get('deadline')
}
}
# Broadcast request
await self.broadcast(requesting_agent, collaboration_request)
# Wait for responses
responses = await self.conversation_manager.wait_for_responses(
requesting_agent,
timeout=30
)
# Return list of agents willing to collaborate
return [r['from_agent'] for r in responses if r['payload'].get('accepted')]
class MessageBus:
"""Message bus for agent communication"""
def __init__(self):
self.subscribers: Dict[str, List[Callable]] = {}
self._lock = asyncio.Lock()
async def subscribe(self, agent_id: str, handler: Callable):
"""Subscribe agent to messages"""
async with self._lock:
if agent_id not in self.subscribers:
self.subscribers[agent_id] = []
self.subscribers[agent_id].append(handler)
async def publish(self, message: 'AgentMessage'):
"""Publish message to recipients"""
handlers = self.subscribers.get(message.to_agent, [])
# Execute handlers concurrently
tasks = [handler(message) for handler in handlers]
await asyncio.gather(*tasks, return_exceptions=True)
@dataclass
class AgentMessage:
id: str
from_agent: str
to_agent: str
type: str
payload: Dict[str, Any]
timestamp: datetime
conversation_id: Optional[str] = None# mcp_server/orchestration/state_manager.py
class DistributedStateManager:
"""Manage shared state across agents"""
def __init__(self, backend: str = 'redis'):
self.backend = self._init_backend(backend)
self.locks: Dict[str, asyncio.Lock] = {}
async def get_state(self, key: str) -> Optional[Dict[str, Any]]:
"""Get state value"""
data = await self.backend.get(key)
return json.loads(data) if data else None
async def set_state(self, key: str, value: Dict[str, Any], ttl: Optional[int] = None):
"""Set state value"""
data = json.dumps(value)
await self.backend.set(key, data, ttl=ttl)
async def update_state(self, key: str, updates: Dict[str, Any]):
"""Atomically update state"""
async with self._get_lock(key):
current = await self.get_state(key) or {}
current.update(updates)
await self.set_state(key, current)
async def increment_counter(self, key: str, amount: int = 1) -> int:
"""Increment a counter atomically"""
return await self.backend.increment(key, amount)
def _get_lock(self, key: str) -> asyncio.Lock:
"""Get or create lock for key"""
if key not in self.locks:
self.locks[key] = asyncio.Lock()
return self.locks[key]
def _init_backend(self, backend_type: str):
"""Initialize storage backend"""
if backend_type == 'redis':
return RedisBackend()
elif backend_type == 'memory':
return MemoryBackend()
else:
raise ValueError(f"Unknown backend: {backend_type}")
class WorkflowState:
"""Manage workflow execution state"""
def __init__(self, state_manager: DistributedStateManager):
self.state_manager = state_manager
async def create_workflow(self, workflow_id: str, definition: Dict[str, Any]):
"""Create new workflow instance"""
state = {
'id': workflow_id,
'definition': definition,
'status': 'initialized',
'current_step': None,
'completed_steps': [],
'context': {},
'created_at': datetime.utcnow().isoformat()
}
await self.state_manager.set_state(f"workflow:{workflow_id}", state)
async def update_step(self, workflow_id: str, step_name: str, status: str, result: Optional[Dict] = None):
"""Update workflow step status"""
updates = {
'current_step': step_name,
'last_updated': datetime.utcnow().isoformat()
}
if status == 'completed':
state = await self.state_manager.get_state(f"workflow:{workflow_id}")
completed_steps = state.get('completed_steps', [])
completed_steps.append({
'name': step_name,
'completed_at': datetime.utcnow().isoformat(),
'result': result
})
updates['completed_steps'] = completed_steps
await self.state_manager.update_state(f"workflow:{workflow_id}", updates)# k8s/mcp-server-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: mainframe-mcp-server
namespace: mcp-system
spec:
replicas: 3
selector:
matchLabels:
app: mcp-server
template:
metadata:
labels:
app: mcp-server
spec:
containers:
- name: mcp-server
image: mainframe-mcp:latest
ports:
- containerPort: 8080
name: mcp
- containerPort: 9090
name: metrics
env:
- name: MCP_CONFIG
valueFrom:
configMapKeyRef:
name: mcp-config
key: config.yaml
- name: AZURE_AI_ENDPOINT
valueFrom:
secretKeyRef:
name: azure-credentials
key: endpoint
resources:
requests:
memory: "2Gi"
cpu: "1"
limits:
memory: "4Gi"
cpu: "2"
livenessProbe:
httpGet:
path: /health
port: 8080
initialDelaySeconds: 30
periodSeconds: 10
readinessProbe:
httpGet:
path: /ready
port: 8080
initialDelaySeconds: 5
periodSeconds: 5
---
apiVersion: v1
kind: Service
metadata:
name: mcp-server
namespace: mcp-system
spec:
selector:
app: mcp-server
ports:
- name: mcp
port: 8080
targetPort: 8080
- name: metrics
port: 9090
targetPort: 9090
type: LoadBalancer
---
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
name: mcp-server-hpa
namespace: mcp-system
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: mainframe-mcp-server
minReplicas: 3
maxReplicas: 20
metrics:
- type: Resource
resource:
name: cpu
target:
type: Utilization
averageUtilization: 70
- type: Resource
resource:
name: memory
target:
type: Utilization
averageUtilization: 80
- type: Pods
pods:
metric:
name: mcp_active_connections
target:
type: AverageValue
averageValue: "100"# mcp_server/security/auth.py
import jwt
from datetime import datetime, timedelta
from typing import Dict, Any, Optional
import hashlib
class MCPSecurityManager:
"""Security manager for MCP server"""
def __init__(self, config: Dict[str, Any]):
self.jwt_secret = config['jwt_secret']
self.token_expiry = config.get('token_expiry_hours', 24)
self.rbac = RBACManager()
async def authenticate_agent(self, credentials: Dict[str, Any]) -> Optional[str]:
"""Authenticate an agent and return JWT token"""
agent_id = credentials.get('agent_id')
api_key = credentials.get('api_key')
# Validate credentials
if not await self._validate_credentials(agent_id, api_key):
return None
# Generate JWT token
payload = {
'agent_id': agent_id,
'roles': await self.rbac.get_agent_roles(agent_id),
'exp': datetime.utcnow() + timedelta(hours=self.token_expiry),
'iat': datetime.utcnow()
}
return jwt.encode(payload, self.jwt_secret, algorithm='HS256')
async def validate_token(self, token: str) -> Optional[Dict[str, Any]]:
"""Validate JWT token"""
try:
payload = jwt.decode(token, self.jwt_secret, algorithms=['HS256'])
return payload
except jwt.ExpiredSignatureError:
return None
except jwt.InvalidTokenError:
return None
async def authorize_action(self, agent_id: str, resource: str, action: str) -> bool:
"""Check if agent is authorized for action"""
roles = await self.rbac.get_agent_roles(agent_id)
for role in roles:
if await self.rbac.check_permission(role, resource, action):
return True
return False
async def _validate_credentials(self, agent_id: str, api_key: str) -> bool:
"""Validate agent credentials"""
# In production, check against secure storage
stored_key_hash = await self._get_stored_key_hash(agent_id)
provided_key_hash = hashlib.sha256(api_key.encode()).hexdigest()
return stored_key_hash == provided_key_hash
class RBACManager:
"""Role-Based Access Control manager"""
def __init__(self):
self.roles = self._init_roles()
self.permissions = self._init_permissions()
def _init_roles(self) -> Dict[str, List[str]]:
"""Initialize role definitions"""
return {
'discovery_agent': ['read:code', 'read:config', 'write:metrics'],
'analysis_agent': ['read:code', 'read:analysis', 'write:analysis'],
'transformation_agent': ['read:code', 'write:code', 'read:analysis'],
'deployment_agent': ['read:all', 'write:deployment', 'execute:pipeline'],
'admin_agent': ['read:all', 'write:all', 'execute:all']
}
def _init_permissions(self) -> Dict[str, Dict[str, List[str]]]:
"""Initialize permission matrix"""
return {
'code': {
'read': ['discovery_agent', 'analysis_agent', 'transformation_agent', 'admin_agent'],
'write': ['transformation_agent', 'admin_agent']
},
'analysis': {
'read': ['analysis_agent', 'transformation_agent', 'admin_agent'],
'write': ['analysis_agent', 'admin_agent']
},
'deployment': {
'read': ['deployment_agent', 'admin_agent'],
'write': ['deployment_agent', 'admin_agent']
}
}
async def get_agent_roles(self, agent_id: str) -> List[str]:
"""Get roles assigned to agent"""
# In production, fetch from database
return ['analysis_agent'] # Default role
async def check_permission(self, role: str, resource: str, action: str) -> bool:
"""Check if role has permission"""
resource_perms = self.permissions.get(resource, {})
allowed_roles = resource_perms.get(action, [])
return role in allowed_roles
# mcp_server/security/encryption.py
class EncryptionManager:
"""Handle encryption for sensitive data"""
def __init__(self, key_vault_url: str):
self.key_vault = AzureKeyVault(key_vault_url)
async def encrypt_payload(self, data: Dict[str, Any]) -> str:
"""Encrypt sensitive payload"""
# Get encryption key from key vault
key = await self.key_vault.get_key('mcp-encryption-key')
# Encrypt data
encrypted = self._encrypt(json.dumps(data), key)
return base64.b64encode(encrypted).decode()
async def decrypt_payload(self, encrypted_data: str) -> Dict[str, Any]:
"""Decrypt sensitive payload"""
# Get decryption key
key = await self.key_vault.get_key('mcp-encryption-key')
# Decrypt data
encrypted_bytes = base64.b64decode(encrypted_data)
decrypted = self._decrypt(encrypted_bytes, key)
return json.loads(decrypted)# mcp_server/monitoring/metrics.py
from prometheus_client import Counter, Histogram, Gauge, Info
import time
class MCPMetrics:
"""Prometheus metrics for MCP server"""
def __init__(self):
# Connection metrics
self.active_connections = Gauge(
'mcp_active_connections',
'Number of active MCP connections'
)
self.connection_total = Counter(
'mcp_connections_total',
'Total number of MCP connections',
['agent_type']
)
# Message metrics
self.messages_processed = Counter(
'mcp_messages_processed_total',
'Total messages processed',
['message_type', 'status']
)
self.message_duration = Histogram(
'mcp_message_duration_seconds',
'Message processing duration',
['message_type']
)
# Tool execution metrics
self.tool_executions = Counter(
'mcp_tool_executions_total',
'Total tool executions',
['tool_name', 'status']
)
self.tool_duration = Histogram(
'mcp_tool_duration_seconds',
'Tool execution duration',
['tool_name']
)
# Agent metrics
self.registered_agents = Gauge(
'mcp_registered_agents',
'Number of registered agents',
['agent_type']
)
self.agent_tasks = Counter(
'mcp_agent_tasks_total',
'Total tasks assigned to agents',
['agent_type', 'task_type', 'status']
)
# System metrics
self.server_info = Info(
'mcp_server',
'MCP server information'
)
self.server_info.info({
'version': '1.0.0',
'language': 'python',
'framework': 'asyncio'
})
def track_connection(self, agent_type: str):
"""Track new connection"""
self.active_connections.inc()
self.connection_total.labels(agent_type=agent_type).inc()
def track_disconnection(self):
"""Track disconnection"""
self.active_connections.dec()
def track_message(self, message_type: str, duration: float, status: str = 'success'):
"""Track message processing"""
self.messages_processed.labels(
message_type=message_type,
status=status
).inc()
self.message_duration.labels(
message_type=message_type
).observe(duration)
def track_tool_execution(self, tool_name: str, duration: float, status: str = 'success'):
"""Track tool execution"""
self.tool_executions.labels(
tool_name=tool_name,
status=status
).inc()
self.tool_duration.labels(
tool_name=tool_name
).observe(duration)
# mcp_server/monitoring/health.py
class HealthChecker:
"""Health check implementation for MCP server"""
def __init__(self, mcp_server: 'MainframeMCPServer'):
self.mcp_server = mcp_server
self.checks = {
'server': self._check_server,
'agents': self._check_agents,
'tools': self._check_tools,
'database': self._check_database,
'ai_connection': self._check_ai_connection
}
async def check_health(self) -> Dict[str, Any]:
"""Perform health check"""
results = {}
overall_status = 'healthy'
for name, check_func in self.checks.items():
try:
result = await check_func()
results[name] = result
if result['status'] != 'healthy':
overall_status = 'unhealthy'
except Exception as e:
results[name] = {
'status': 'error',
'message': str(e)
}
overall_status = 'unhealthy'
return {
'status': overall_status,
'timestamp': datetime.utcnow().isoformat(),
'checks': results
}
async def _check_server(self) -> Dict[str, Any]:
"""Check MCP server status"""
return {
'status': 'healthy',
'active_connections': len(self.mcp_server.agents),
'registered_tools': len(self.mcp_server.tools),
'uptime_seconds': time.time() - self.mcp_server.start_time
}
async def _check_agents(self) -> Dict[str, Any]:
"""Check agent health"""
total_agents = len(self.mcp_server.agents)
active_agents = sum(1 for a in self.mcp_server.agents.values() if a.status == 'active')
status = 'healthy' if active_agents > 0 else 'degraded'
return {
'status': status,
'total': total_agents,
'active': active_agents
}
async def _check_ai_connection(self) -> Dict[str, Any]:
"""Check Azure AI connection"""
try:
# Test AI connection
response = await self.mcp_server.ai_client.health_check()
return {
'status': 'healthy' if response else 'unhealthy',
'endpoint': self.mcp_server.ai_client.endpoint
}
except Exception as e:
return {
'status': 'unhealthy',
'error': str(e)
}# mcp_server/integrations/azure_ai.py
from azure.ai.foundry import AIFoundryClient
from azure.identity import DefaultAzureCredential
class AzureAIIntegration:
"""Integration with Azure AI Foundry for MCP"""
def __init__(self, config: Dict[str, Any]):
self.credential = DefaultAzureCredential()
self.client = AIFoundryClient(
endpoint=config['endpoint'],
credential=self.credential
)
self.models = {
'code_understanding': config.get('code_model', 'gpt-4-32k'),
'transformation': config.get('transform_model', 'codex-mainframe'),
'analysis': config.get('analysis_model', 'gpt-4-turbo')
}
async def enhance_code_analysis(self, code: str, language: str, context: Dict[str, Any]) -> Dict[str, Any]:
"""Use AI to enhance code analysis"""
prompt = self._build_analysis_prompt(code, language, context)
response = await self.client.completions.create(
model=self.models['analysis'],
messages=[
{"role": "system", "content": "You are an expert mainframe code analyst."},
{"role": "user", "content": prompt}
],
temperature=0.1,
max_tokens=4000
)
return self._parse_analysis_response(response)
async def generate_transformation_plan(self, analysis: Dict[str, Any]) -> Dict[str, Any]:
"""Generate transformation plan using AI"""
prompt = f"""
Based on this mainframe code analysis, create a detailed transformation plan:
Analysis: {json.dumps(analysis, indent=2)}
Include:
1. Transformation strategy
2. Risk assessment
3. Estimated effort
4. Recommended target architecture
5. Step-by-step migration plan
"""
response = await self.client.completions.create(
model=self.models['transformation'],
messages=[{"role": "user", "content": prompt}],
temperature=0.3
)
return self._parse_transformation_plan(response)
async def assist_code_transformation(self, source_code: str, source_lang: str, target_lang: str) -> str:
"""AI-assisted code transformation"""
prompt = f"""
Transform this {source_lang} code to {target_lang}:
```{source_lang}
{source_code}
```
Requirements:
- Preserve business logic exactly
- Use modern {target_lang} patterns
- Add appropriate error handling
- Include unit tests
- Add documentation
"""
response = await self.client.completions.create(
model=self.models['transformation'],
messages=[{"role": "user", "content": prompt}],
temperature=0.1,
max_tokens=8000
)
return response.choices[0].message.content
# mcp_server/integrations/github_copilot.py
class GitHubCopilotIntegration:
"""Integration with GitHub Copilot for code suggestions"""
def __init__(self, config: Dict[str, Any]):
self.workspace_path = config['workspace_path']
self.patterns = self._load_patterns()
def _load_patterns(self) -> Dict[str, List[Dict]]:
"""Load mainframe transformation patterns"""
return {
'cobol': [
{
'pattern': r'PERFORM\s+(\w+)\s+UNTIL\s+(.+)',
'suggestion': 'while (!{1}) {{ {0}(); }}'
},
{
'pattern': r'MOVE\s+(.+)\s+TO\s+(.+)',
'suggestion': '{1} = {0};'
}
],
'natural': [
{
'pattern': r'FIND\s+(\w+)\s+WITH\s+(.+)',
'suggestion': 'repository.findBy{0}({1})'
},
{
'pattern': r'READ\s+(\w+)\s+BY\s+ISN',
'suggestion': 'repository.findById(id)'
}
]
}
def create_copilot_config(self, language: str) -> Dict[str, Any]:
"""Create Copilot configuration for language"""
patterns = self.patterns.get(language, [])
return {
'version': '1.0',
'language': language,
'patterns': patterns,
'context': {
'framework': 'spring-boot' if language in ['cobol', 'natural'] else 'generic',
'testing': 'junit5',
'build': 'maven'
}
}
# mcp_server/integrations/devops.py
class DevOpsIntegration:
"""Integration with GitHub Actions and Azure DevOps"""
def __init__(self, platform: str, config: Dict[str, Any]):
self.platform = platform
self.config = config
async def create_pipeline(self, project: Dict[str, Any]) -> str:
"""Create CI/CD pipeline for modernized application"""
if self.platform == 'github':
return await self._create_github_pipeline(project)
elif self.platform == 'azure-devops':
return await self._create_azure_pipeline(project)
async def _create_github_pipeline(self, project: Dict[str, Any]) -> str:
"""Create GitHub Actions workflow"""
workflow = f"""
name: Mainframe Modernization Pipeline
on:
push:
branches: [main, develop]
pull_request:
branches: [main]
jobs:
analysis:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: MCP Agent Analysis
uses: mainframe-modernization/mcp-action@v1
with:
mcp-server: ${{{{ secrets.MCP_SERVER_URL }}}}
agent-type: analysis
language: {project['source_language']}
- name: Security Scan
uses: github/advanced-security/codeql-action@v2
transformation:
needs: analysis
runs-on: ubuntu-latest
steps:
- name: Transform Code
uses: mainframe-modernization/mcp-action@v1
with:
mcp-server: ${{{{ secrets.MCP_SERVER_URL }}}}
agent-type: transformation
source-language: {project['source_language']}
target-language: {project['target_language']}
test:
needs: transformation
runs-on: ubuntu-latest
steps:
- name: Run Tests
run: |
mvn test
mvn integration-test
- name: Performance Test
uses: mainframe-modernization/performance-test@v1
deploy:
needs: test
runs-on: ubuntu-latest
if: github.ref == 'refs/heads/main'
steps:
- name: Deploy to Azure
uses: azure/webapps-deploy@v2
with:
app-name: {project['app_name']}
publish-profile: ${{{{ secrets.AZURE_PUBLISH_PROFILE }}}}
"""
return workflow# mcp_server/patterns/implementation_patterns.py
class MCPPatterns:
"""Common patterns for MCP implementation"""
@staticmethod
def agent_factory_pattern():
"""Factory pattern for creating agents"""
class AgentFactory:
_agents = {
'discovery': DiscoveryAgent,
'analysis': AnalysisAgent,
'transformation': TransformationAgent,
'testing': TestingAgent,
'deployment': DeploymentAgent
}
@classmethod
def create_agent(cls, agent_type: str, config: Dict[str, Any]) -> 'BaseAgent':
agent_class = cls._agents.get(agent_type)
if not agent_class:
raise ValueError(f"Unknown agent type: {agent_type}")
return agent_class(config)
@staticmethod
def circuit_breaker_pattern():
"""Circuit breaker for handling failures"""
class CircuitBreaker:
def __init__(self, failure_threshold: int = 5, timeout: int = 60):
self.failure_threshold = failure_threshold
self.timeout = timeout
self.failure_count = 0
self.last_failure_time = None
self.state = 'closed' # closed, open, half-open
async def call(self, func, *args, **kwargs):
if self.state == 'open':
if time.time() - self.last_failure_time > self.timeout:
self.state = 'half-open'
else:
raise Exception("Circuit breaker is open")
try:
result = await func(*args, **kwargs)
if self.state == 'half-open':
self.state = 'closed'
self.failure_count = 0
return result
except Exception as e:
self.failure_count += 1
self.last_failure_time = time.time()
if self.failure_count >= self.failure_threshold:
self.state = 'open'
raise e
@staticmethod
def retry_pattern():
"""Exponential backoff retry pattern"""
def retry_with_backoff(max_retries: int = 3, base_delay: float = 1.0):
def decorator(func):
async def wrapper(*args, **kwargs):
for attempt in range(max_retries):
try:
return await func(*args, **kwargs)
except Exception as e:
if attempt == max_retries - 1:
raise e
delay = base_delay * (2 ** attempt)
await asyncio.sleep(delay)
return wrapper
return decorator# mcp_server/patterns/error_handling.py
class MCPErrorHandler:
"""Centralized error handling for MCP"""
def __init__(self):
self.error_handlers = {
ConnectionError: self._handle_connection_error,
TimeoutError: self._handle_timeout_error,
ValidationError: self._handle_validation_error,
AuthenticationError: self._handle_auth_error
}
async def handle_error(self, error: Exception, context: Dict[str, Any]) -> Dict[str, Any]:
"""Handle errors with appropriate recovery strategies"""
error_type = type(error)
handler = self.error_handlers.get(error_type, self._handle_generic_error)
return await handler(error, context)
async def _handle_connection_error(self, error: ConnectionError, context: Dict[str, Any]):
"""Handle connection errors"""
return {
'action': 'retry',
'delay': 5,
'max_retries': 3,
'fallback': 'use_cached_data'
}
async def _handle_timeout_error(self, error: TimeoutError, context: Dict[str, Any]):
"""Handle timeout errors"""
return {
'action': 'increase_timeout',
'new_timeout': context.get('timeout', 30) * 2,
'alert': 'performance_degradation'
}# mcp_server/patterns/performance.py
class PerformanceOptimizer:
"""Performance optimization strategies for MCP"""
@staticmethod
def connection_pooling():
"""Connection pool for agent connections"""
class ConnectionPool:
def __init__(self, max_size: int = 100):
self.pool = asyncio.Queue(maxsize=max_size)
self.size = 0
self.max_size = max_size
async def acquire(self):
if self.pool.empty() and self.size < self.max_size:
conn = await self._create_connection()
self.size += 1
else:
conn = await self.pool.get()
return conn
async def release(self, conn):
await self.pool.put(conn)
@staticmethod
def caching_strategy():
"""Intelligent caching for MCP operations"""
class SmartCache:
def __init__(self, ttl: int = 300):
self.cache = {}
self.ttl = ttl
async def get_or_compute(self, key: str, compute_func):
if key in self.cache:
entry = self.cache[key]
if time.time() - entry['timestamp'] < self.ttl:
return entry['value']
value = await compute_func()
self.cache[key] = {
'value': value,
'timestamp': time.time()
}
return valueThis chapter has provided a comprehensive guide to implementing MCP-Enabled Agent Architecture for mainframe modernization. Key topics covered include:
- MCP Fundamentals: Understanding the protocol and its benefits for mainframe modernization
- Server Implementation: Building a production-ready MCP server with language-specific contexts
- Multi-Agent Orchestration: Implementing sophisticated agent coordination and task distribution
- Production Deployment: Kubernetes deployment, security, and monitoring
- Integrations: Connecting with Azure AI Platform, GitHub Copilot, and DevOps tools
- Best Practices: Patterns for reliability, performance, and maintainability
The MCP architecture enables organizations to build scalable, intelligent mainframe modernization solutions that leverage the power of AI agents while maintaining security, reliability, and performance.
- Chapter 16: Agentic DevOps for Mainframe - Learn about self-healing pipelines and autonomous operations
- Workshop: Implementing MCP - Hands-on exercises for building MCP solutions