CodeGraph CLI MCP Server

--- pdf-engine: lualatex mainfont: "DejaVu Serif" monofont: "DejaVu Sans Mono" header-includes: | \usepackage{fontspec} \directlua{ luaotfload.add_fallback("emojifallback", {"NotoColorEmoji:mode=harf;"}) } \setmainfont[ RawFeature={fallback=emojifallback} ]{DejaVu Serif} --- # CodeGraph MCP Protocol Specification ## Executive Summary The CodeGraph Model Context Protocol (MCP) Server provides a comprehensive multi-agent coordination framework built on the MCP specification. It enables real-time WebSocket-based communication, distributed agent management, and sophisticated message routing for collaborative code analysis and development workflows. ## Architecture Overview ### Core Components 1. **Transport Layer**: WebSocket-based transport with fallback support 2. **Agent Registry**: Distributed agent discovery and lifecycle management 3. **Message Router**: Protocol-compliant routing with validation 4. **Coordination Engine**: Multi-agent orchestration patterns 5. **Security Layer**: Authentication, authorization, and message validation ### Protocol Compliance - **MCP Version**: 2025-06-18 (Latest) - **Transport**: WebSocket with HTTP fallback - **Message Format**: JSON-RPC 2.0 - **Session Management**: Persistent sessions with reconnection support ## Transport Layer Architecture ### WebSocket Transport Features - Full-duplex real-time communication - Session persistence with unique identifiers - Automatic reconnection with exponential backoff - Message queuing during disconnections - Health monitoring and heartbeat mechanisms ### Multi-Transport Support ```typescript interface TransportConfig { primary: 'websocket' | 'streamable-http' | 'stdio'; fallback?: 'streamable-http' | 'sse'; timeout: number; retryAttempts: number; heartbeatInterval: number; } ``` ### Session Management - UUID-based session identification - Session state persistence across disconnections - Resource cleanup on session termination - Cross-session message routing capabilities ## Agent SDK Design Patterns ### Core Agent Interface ```typescript interface CodeGraphAgent { id: string; type: AgentType; capabilities: AgentCapability[]; status: AgentStatus; metadata: AgentMetadata; // Lifecycle methods initialize(config: AgentConfig): Promise<void>; start(): Promise<void>; stop(): Promise<void>; destroy(): Promise<void>; // Communication methods sendMessage(message: AgentMessage): Promise<void>; broadcast(message: AgentMessage): Promise<void>; subscribe(topic: string): Promise<void>; unsubscribe(topic: string): Promise<void>; } ``` ### Agent Types and Capabilities ```typescript enum AgentType { COORDINATOR = 'coordinator', ANALYZER = 'analyzer', TRANSFORMER = 'transformer', VALIDATOR = 'validator', REPORTER = 'reporter' } interface AgentCapability { name: string; version: string; description: string; inputSchema: JSONSchema; outputSchema: JSONSchema; } ``` ### Agent Discovery and Registration - Automatic agent discovery via multicast - Manual registration with capability declaration - Health check mechanisms - Dynamic capability updates ## Protocol Message Validation and Routing ### Message Schema Validation All messages conform to JSON-RPC 2.0 with CodeGraph extensions: ```json { "jsonrpc": "2.0", "method": "codegraph/agent/coordinate", "params": { "agentId": "uuid", "sessionId": "uuid", "payload": { "type": "task_assignment", "data": {} } }, "id": "request-uuid" } ``` ### Routing Engine ```typescript interface MessageRouter { route(message: MCPMessage): Promise<RouteResult>; addRoute(pattern: RoutePattern, handler: RouteHandler): void; removeRoute(pattern: RoutePattern): void; // Advanced routing features routeWithLoadBalancing(message: MCPMessage): Promise<RouteResult>; routeWithFailover(message: MCPMessage): Promise<RouteResult>; broadcastToGroup(group: string, message: MCPMessage): Promise<void>; } ``` ### Message Types 1. **Control Messages**: Agent lifecycle, discovery, heartbeat 2. **Task Messages**: Work assignment, progress updates, results 3. **Coordination Messages**: Inter-agent communication, synchronization 4. **System Messages**: Error handling, logging, metrics ## Multi-Agent Communication Patterns ### Communication Topologies #### 1. Hub-and-Spoke Pattern - Central coordinator manages all agent interactions - Simplified routing and conflict resolution - Single point of failure concerns #### 2. Mesh Pattern - Direct peer-to-peer agent communication - Distributed load and resilience - Complex routing and consistency challenges #### 3. Hierarchical Pattern - Tree-structured agent organization - Delegated coordination responsibilities - Balanced complexity and scalability #### 4. Pipeline Pattern - Sequential processing chain - Optimized for linear workflows - Built-in backpressure handling ### Coordination Mechanisms ```typescript interface CoordinationEngine { // Task distribution distributeTask(task: Task, agents: Agent[]): Promise<TaskResult[]>; // Synchronization synchronize(agents: Agent[], barrier: string): Promise<void>; // Consensus achieveConsensus(agents: Agent[], proposal: Proposal): Promise<ConsensusResult>; // Load balancing balanceLoad(tasks: Task[], agents: Agent[]): Promise<LoadBalanceResult>; } ``` ### Event-Driven Architecture - Pub/Sub messaging for loose coupling - Event streaming with replay capabilities - Dead letter queues for failed messages - Circuit breaker patterns for resilience ## Security and Authentication ### Transport Security - TLS/WSS encryption for all communications - Certificate pinning for client validation - Token-based authentication with JWT - Rate limiting and DDoS protection ### Message Security - Message signing with Ed25519 keys - Payload encryption for sensitive data - Replay attack prevention with nonces - Role-based access control (RBAC) ### Agent Security - Agent identity verification - Capability-based permissions - Sandboxed execution environments - Audit logging for all actions ## Performance and Scalability ### Horizontal Scaling - Stateless server design - Load balancer integration - Database sharding strategies - Distributed caching with Redis ### Performance Optimizations - Message batching and compression - Connection pooling - Lazy loading of agent capabilities - Asynchronous processing pipelines ### Monitoring and Metrics - Real-time performance dashboards - Agent health monitoring - Message throughput tracking - Error rate analysis ## Implementation Specifications ### Required Dependencies - WebSocket server (ws library) - JSON-RPC 2.0 implementation - Schema validation (Ajv) - Authentication (JWT) - Database (PostgreSQL/MongoDB) - Message queue (Redis/RabbitMQ) ### Configuration Management - Environment-based configuration - Runtime configuration updates - Feature flags for gradual rollouts - A/B testing framework integration ### Error Handling - Comprehensive error classification - Graceful degradation strategies - Automatic recovery mechanisms - Detailed error reporting ### Testing Strategy - Unit tests for core components - Integration tests for message flows - Load testing for performance validation - Chaos engineering for resilience testing ## API Endpoints ### Core MCP Endpoints - `POST /mcp` - Main message handling - `GET /mcp` - SSE notifications - `DELETE /mcp` - Session termination - `WS /ws` - WebSocket upgrade ### CodeGraph Extensions - `GET /agents` - List registered agents - `POST /agents/register` - Agent registration - `DELETE /agents/{id}` - Agent deregistration - `GET /coordination/status` - System status - `POST /coordination/tasks` - Task submission ## Deployment Considerations ### Infrastructure Requirements - Kubernetes cluster with ingress - Load balancer with WebSocket support - Persistent storage for session data - Monitoring and logging stack ### High Availability Setup - Multi-region deployment - Database replication - Circuit breakers and failover - Health checks and auto-recovery ### Development Workflow - Docker containerization - CI/CD pipeline integration - Infrastructure as Code (Terraform) - Environment promotion strategy ## Future Enhancements ### Planned Features - Visual agent topology designer - Machine learning-based load prediction - Advanced consensus algorithms - Cross-language agent support ### Research Areas - Quantum-resistant cryptography - Federated learning integration - Edge computing deployment - AI-driven coordination optimization --- This specification provides the foundation for implementing a production-ready CodeGraph MCP server that enables sophisticated multi-agent coordination while maintaining full compliance with the Model Context Protocol standard.