CodeAnalysis MCP Server

# MCP Code Analysis Enhancement Plan ## Overview This document outlines the strategic plan for enhancing the MCP (Model Context Protocol) server for code analysis. The goal is to optimize the server for AI-assisted development, ensuring tools are easily discoverable, well-documented, and produce consistent, useful results for AI agents. ## Areas for Enhancement ### 1. Tool Description & Documentation - **Goal**: Improve AI understanding of available tools and their usage - **Action Items**: - ✅ Add comprehensive JSDoc comments to all tool registration functions - ✅ Create standardized parameter descriptions across all tools - ✅ Implement consistent "examples" field in all tool descriptions - ✅ Generate automated documentation from tool schemas ### 2. Result Formatting - **Goal**: Standardize tool outputs for optimal AI consumption - **Action Items**: - ✅ Define a consistent JSON output schema for all tools - ✅ Include metadata in responses (timing, version, etc.) - ✅ Format complex data for easier parsing (nested objects, typed arrays) - Add semantic markers to highlight important parts of results ### 3. Tool Composition - **Goal**: Enable multi-step analysis workflows - **Action Items**: - Create a workflow engine to chain tool executions - Implement preset workflows for common code analysis patterns - Add parameterization of workflows for flexibility - Build a workflow visualization component ### 4. Context Sharing - **Goal**: Maintain state across sequential tool invocations - **Action Items**: - ✅ Implement a session management system - Create a caching layer for analysis results - ✅ Add context persistence between related operations - Enable cross-referencing between tool results ### 5. Testing Framework - **Goal**: Ensure reliability of tools when used by AI - **Action Items**: - ✅ Create unit tests for each individual tool - Implement integration tests for tool combinations - ✅ Add performance benchmarks for response times - Build test fixtures for common code patterns ## Technology Choices ### Hybrid Architecture Approach We'll implement a hybrid architecture to optimize for both development speed and performance: 1. **TypeScript for Server Core** - MCP server infrastructure and API layer - Tool registration and orchestration - Integration with MCP SDK - Developer experience tooling - State management (XState) and high-level workflow orchestration - API and service layers 2. **Rust for Performance-Critical Components** - Code parsers and analyzers - Graph traversal algorithms - Memory-intensive operations - High-performance metric calculations - Implemented as standalone binaries or WASM modules - Focus on tools where performance matters most (metrics, parsing, static analysis) 3. **Python for ML/AI Components** - Code classification models - Suggestion generation - Pattern detection - Integration with ML libraries - Leveraging existing ML ecosystems for code understanding ### Implementation Strategy We're adopting a tailored approach for each component in our architecture: 1. **Session Context**: Redis + XState integration - Redis for distributed state persistence - XState for state machine modeling - Session sharing across multiple server instances 2. **Tool Composition**: XState-driven workflow engine - Define reusable workflow templates - State machines for complex tool sequences - Visual workflow debugging and monitoring 3. **Performance-Critical Analysis**: Rust modules with TypeScript wrappers - Native code for performance-intensive operations - Consistent TypeScript interfaces for all tools - WASM packaging for browser compatibility when needed 4. **Knowledge Graph**: Neo4j or specialized graph database - Model code relationships as a connected graph - Efficient traversal and pattern matching - Support for complex relationship queries ### State Management with XState We'll use XState for modeling complex AI agent states and workflows: - **Statechart Modeling**: Formally define agent states and transitions - **Visualization**: Use XState's visualization tools for debugging and documentation - **Context Management**: Leverage XState's built-in context for maintaining state - **Composition**: Create modular, composable state machines for different analysis flows ### AI-Tool Synchronization We'll implement multiple synchronization mechanisms: 1. **Event-Based Architecture** - Pub/sub system for tool completion events - WebSocket support for real-time updates - Event replay for state reconstruction 2. **Caching Layer** - Redis for distributed caching - Versioned cache entries - TTL-based expiration strategy ### Schema Validation with Zod We'll use Zod for comprehensive schema validation: - **Request Validation**: Validate tool inputs with detailed error messages - **Response Validation**: Ensure all tool responses conform to our standard format - **Schema Generation**: Automatically generate documentation from Zod schemas - **Runtime Type Safety**: Leverage TypeScript integration for type inference ### Component Implementation | Component | Primary Technology | Supporting Technologies | | -------------------- | ------------------- | ------------------------- | | Server Core | TypeScript | Express, MCP SDK | | State Management | XState | Redis | | Schema Validation | Zod | TypeScript | | Tool Composition | TypeScript + XState | - | | Performance Analysis | Rust | WASM, TypeScript bindings | | Knowledge Graph | TypeScript | Neo4j, TypeScript | | Code Parsing | Rust | Tree-sitter | | ML Components | Python | TensorFlow/PyTorch | | Testing Framework | Vitest | - | ## Implementation Recommendations ### 1. Standardize Tool Response Format ```typescript // Example standardized response format with Zod schema import { z } from "zod"; // Define the schema with Zod export const ToolResponseSchema = z.object({ data: z.any(), metadata: z.object({ tool: z.string(), version: z.string(), executionTime: z.number(), timestamp: z.string(), }), status: z.object({ success: z.boolean(), code: z.number(), message: z.string().optional(), }), context: z .object({ sessionId: z.string().optional(), relatedResults: z.array(z.string()).optional(), }) .optional(), }); // Type derived from the schema export type ToolResponse<T> = z.infer<typeof ToolResponseSchema> & { data: T; }; // Helper function for creating valid responses export function createToolResponse<T>( data: T, tool: string, success = true, contextData?: { sessionId?: string; relatedResults?: string[] } ): ToolResponse<T> { return { data, metadata: { tool, version: "1.0.0", // Should be dynamically pulled from package executionTime: 0, // Should be calculated timestamp: new Date().toISOString(), }, status: { success, code: success ? 200 : 400, message: success ? undefined : "An error occurred", }, context: contextData, }; } ``` - ✅ Implement this interface across all tool responses - ✅ Create helper functions for generating standard responses - ✅ Add TypeScript types for all response objects - ✅ Use Zod for runtime validation of responses ### 2. Tool Discovery Mechanism - ✅ Create a new MCP tool `list-available-tools` that returns: - All registered tools with descriptions - Parameter schemas and examples - Categories and tags for filtering - Implement a tool capability graph showing relationships - Add search functionality for finding tools by capability ### 3. Session Context Implementation - ✅ Create a session management system with: - Session creation/termination API - State persistence between calls - Timeout and garbage collection - Context sharing between related tools - Implement through Redis or similar for scalability - ✅ Integrate with XState for state management ### 4. Tool Documentation Expansion - For each tool parameter, add: - ✅ Detailed description - ✅ Expected format - ✅ Example values - Common errors - Generate interactive documentation site from schemas - ✅ Auto-generate examples from Zod schemas ### 5. AI-Specific Workflows Create composition tools for common AI tasks: - `analyze-code-quality` - Combines metrics, linting, and best practices - `refactor-suggestion` - Analyzes code and suggests improvements - `dependency-impact` - Analyzes change impact across codebase - `architecture-overview` - Generates high-level system understanding ## Implementation Timeline ### Phase 1: Foundation (Complete) ✅ - ✅ Standardize response formats with Zod schemas - Created consistent success, error, and partial response formats - Implemented Zod schemas for response validation - Added utility functions for generating responses - Ensured TypeScript type inference from schemas - ✅ Implement basic tool documentation improvements - Added JSDoc comments with examples - Created standardized parameter descriptions - Implemented schema-based documentation - Used Zod describe() for rich parameter metadata - ✅ Create initial test framework - Set up Vitest for testing - Implemented mock MCP server - Created shared test utilities - Added proper lifecycle management in tests - ✅ Set up XState for core state management - Implemented tool machine with typed states - Created tool execution service - Added parameter validation - Implemented execution history tracking - ✅ Implement stateful tool support - Added session management - Created state persistence between calls - Modified schemas to include sessionId - Provided helper functions for session interaction - ✅ Contract validation and verification - Implemented API contract verification - Added runtime schema validation - Created integration tests for contract adherence - Documented interface contracts ### Phase 2: Redis Integration and Performance Optimization (Weeks 3-4) - **Redis Session Management** - Implement Redis-backed session store - Add distributed session locking - Create TTL-based session expiration - Implement session migration utilities - **Performance Optimization** - Profile and optimize tool execution - Implement caching for expensive operations - Add batch processing capabilities - Optimize state serialization and deserialization - **Tool Discovery and Metadata** - Create comprehensive tool registry - Add rich metadata to tool definitions - Implement tool search and filtering - Create tool relationship graph - **Initial Rust-based Tools** - Implement code parsing using Tree-sitter - Create code complexity analyzer - Build dependency graph generator - Add performance benchmarking tool - **Enhanced Error Handling** - Create standardized error codes - Add detailed error context - Implement error recovery strategies - Add error logging and aggregation ### Phase 3: Advanced Capabilities and Workflows (Weeks 5-8) - **Workflow Engine with XState** - Create workflow definitions with XState - Implement workflow persistence - Add parameterized workflow templates - Create workflow visualization - **Advanced Analysis Tools** - Implement code quality scoring - Add semantic code understanding - Create refactoring suggestion tools - Build architecture visualization - **Integration Testing Framework** - Create integration test fixtures - Implement end-to-end test scenarios - Add performance regression tests - Create compatibility test suite - **API Gateway and Load Balancing** - Implement API gateway for tool access - Add request rate limiting - Create load balancing for heavy tools - Implement prioritization for critical operations - **Documentation and Developer Experience** - Create interactive documentation site - Add visualizations for tool capabilities - Implement playground for tool experimentation - Create detailed tutorials and examples ## Phase 2 Technical Details ### Redis Session Store Implementation We'll implement a Redis-backed session store with the following capabilities: ```typescript export interface RedisSessionStoreOptions { redisUrl: string; prefix?: string; defaultTtl?: number; // in seconds lockTimeout?: number; // in milliseconds } export class RedisSessionStore implements SessionStore { constructor(options: RedisSessionStoreOptions); // Core SessionStore interface async getSession<T>(sessionId: string): Promise<T | null>; async setSession<T>(sessionId: string, state: T, ttl?: number): Promise<void>; async clearSession(sessionId: string): Promise<void>; async getSessions(): Promise<string[]>; // Extended capabilities async acquireLock( sessionId: string, timeout?: number ): Promise<string | null>; async releaseLock(sessionId: string, lockToken: string): Promise<boolean>; async extendSessionTtl(sessionId: string, ttl: number): Promise<boolean>; async getSessionTtl(sessionId: string): Promise<number | null>; async createSessionIfNotExists<T>( sessionId: string, initialState: T ): Promise<T>; } ``` ### Performance Optimization Strategy For Phase 2, we'll focus on the following performance optimizations: 1. **Caching Layer** - Implement Redis-backed result caching - Add cache invalidation strategies - Create tiered caching (memory + Redis) - Implement cache warming for common operations 2. **State Optimization** - Reduce serialization overhead - Implement incremental state updates - Add lazy loading for context data - Optimize state machine transitions 3. **Batch Processing** - Add support for batch tool execution - Implement parallel execution where applicable - Create queue management for heavy operations - Add prioritization for interactive operations 4. **Memory Management** - Implement streaming for large results - Add memory usage monitoring - Create auto-scaling session cleanup - Optimize object pooling for frequent operations ### Rust-based Tool Integration We'll implement a standard pattern for integrating Rust-based tools: ```typescript import { spawnSync } from "child_process"; import { z } from "zod"; import { createStatefulTool } from "../statefulTool"; const ComplexityAnalysisSchema = z.object({ filePath: z.string().describe("Path to the file to analyze"), options: z .object({ metrics: z .array(z.enum(["cyclomatic", "cognitive", "halstead"])) .default(["cyclomatic"]), threshold: z .number() .optional() .describe("Complexity threshold for highlighting"), }) .optional(), }); export const complexityAnalysisTool = createStatefulTool({ name: "complexityAnalysis", description: "Analyzes code complexity using various metrics", schema: ComplexityAnalysisSchema, handler: async (params, context) => { try { const result = spawnSync("./bin/complexity_analyzer", [ params.filePath, "--format=json", `--metrics=${params.options?.metrics.join(",") || "cyclomatic"}`, params.options?.threshold ? `--threshold=${params.options.threshold}` : "", ]); if (result.status !== 0) { return createErrorResponse({ message: "Complexity analysis failed", code: "COMPLEXITY_ANALYSIS_ERROR", details: result.stderr.toString(), }); } const analysisResult = JSON.parse(result.stdout.toString()); return createSuccessResponse(analysisResult); } catch (error) { return createErrorResponse({ message: "Failed to execute complexity analysis", code: "EXECUTION_ERROR", details: error instanceof Error ? error.message : String(error), }); } }, }); ``` ## Success Metrics We will consider Phase 2 successful when we achieve the following metrics: 1. **Performance** - Response time under 100ms for 95% of tool executions - Support for 5000+ concurrent sessions - Memory usage stable with increasing sessions 2. **Reliability** - 99.9% success rate for tool executions - Zero memory leaks in extended testing - Graceful error handling for all edge cases 3. **Developer Experience** - 90% test coverage for core components - Comprehensive documentation for all tools - Interactive example for each tool 4. **Scalability** - Linear scaling with added resources - Support for distributed deployment - Efficient resource utilization ## Next Steps 1. Review and approve this enhancement plan 2. Prioritize specific action items 3. Assign implementation tasks to team members 4. Set up tracking and reporting for progress ## Tech Debt The following items represent technical debt that should be addressed in future iterations. ### 1. Redis Connectivity Issues - **Issue**: While Redis availability checks pass with simple ping tests, actual session operations fail with connection errors. - **Current State**: `redis-cli ping` returns successfully, but Redis session store operations fail with "Redis client not connected" errors. - **Workaround**: Using memory session store temporarily by setting `preferMemory: true` or `FORCE_MEMORY_SESSION=true`. - **Impact**: - Memory store is fully functional for development and single-instance deployments - Missing Redis persistence for production scenarios with multiple instances - Potential data loss on server restarts with memory store #### Investigation Plan 1. **Redis Configuration Check**: - Review Redis server configuration for authentication requirements - Check connection limits and timeout settings - Verify Redis is not in protected mode that restricts connections 2. **Network/Firewall Issues**: - Confirm Redis port (6379) is not being blocked - Check for any network policies affecting Redis connectivity 3. **Connection Handling Improvements**: - Review Redis client initialization in `RedisSessionStore` - Implement better connection retry logic - Add more detailed connection error logging 4. **Client Library Compatibility**: - Verify compatibility between Redis server version and Node.js client - Check for known issues in the Redis client library #### Resolution Timeline - **Short-term**: Continue using memory store for development - **Medium-term**: Implement improved Redis connection error handling and diagnostics - **Long-term**: Provide additional backend options (e.g., SQLite, file-based) for greater flexibility This tech debt should be prioritized before deploying to production environments that require state persistence across multiple instances or server restarts.