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The MCP TypeScript Analyzer (@r-mcp/static-analysis) solves a critical gap in AI-assisted development: enabling language models to deeply understand TypeScript codebases without manual context gathering or build pipeline overhead. Built on ts-morph and the Model Context Protocol, it provides AI assistants with semantic understanding—function signatures, type relationships, cross-file references, and compilation diagnostics—optimized for performance through intelligent caching, parallel processing, and memory management.
Skills
Key Deliverables
- Implemented 4 core analysis tools (file, symbol, references, diagnostics)
- Designed intelligent caching and parallel processing system
- Built memory management for enterprise-scale codebases
- Integrated with MCP protocol for IDE compatibility
- Achieved sub-second query response times
The Problem: Context Blindness in AI Development
Why language models struggle with TypeScript codebases
Large language models excel at generating code, but they struggle with context-aware development in complex codebases. Traditional approaches force developers into one of three inefficient patterns: manually copying-pasting relevant files (losing productivity and completeness), performing real-time static analysis during inference (creating latency and unreliability), or requiring pre-compilation builds (adding overhead and interrupting workflows).
TypeScript compounds the problem. Its rich type system—with generics, unions, intersections, conditional types, and recursive types—creates an explosion of edge cases that make programmatic analysis exceptionally difficult. The TypeScript Compiler API, while powerful, is notoriously complex with verbose patterns and steep learning curves that discourage custom tooling.
Beyond technical barriers, the lack of standardized protocols meant each AI tool built custom integrations. This fragmentation prevented AI assistants from accessing critical compile-time information like symbol types, cross-file references, and semantic errors—information essential for intelligent refactoring, bug detection, and architectural guidance.
The Solution: Bridging AI and Type Information
A standardized, high-performance protocol for TypeScript analysis
The MCP TypeScript Analyzer provides four focused tools that AI assistants can invoke to deeply understand codebases: file analysis for structure, symbol inspection for type information, reference tracking for impact analysis, and compilation diagnostics for error detection. All optimized for sub-second performance and enterprise scale.
Architecture: Three Strategic Decisions
1. ts-morph Over Raw Compiler API
While the TypeScript Compiler API provides complete control, ts-morph trades raw flexibility for developer velocity and maintainability. It abstracts AST manipulation complexity, provides simplified type system access, and handles cross-platform compatibility—enabling rapid feature development while maintaining sophisticated type-aware analysis.
2. MCP Protocol Standardization
By implementing Anthropic's Model Context Protocol, the analyzer works seamlessly across multiple platforms—Cursor IDE, Claude Desktop, Claude Code CLI, and any future MCP-compatible client—without custom integration code. The JSON-RPC 2.0 protocol handles communication across STDIO or HTTP+Server-Sent Events.
3. Performance Engineering at Scale
Enterprise codebases with 10,000+ files require sophisticated optimization: intelligent caching (AST in memory with timestamp-based invalidation), parallel processing (concurrent file analysis with configurable concurrency limits), memory management (monitoring, automatic cache eviction at thresholds), and error recovery (graceful degradation on malformed code).
Four Core Capabilities
Focused tools that cover AI development workflows
1. File Analysis: Understanding Module Structure
Extract comprehensive metadata from TypeScript files without compilation: symbol enumeration (functions, classes, interfaces, types), dependency mapping (imports with sources), export analysis (public API surface), and configurable depth (symbol-only, dependency-only, or complete). AI assistants rapidly understand module structure for refactoring or architectural reviews.
2. Symbol Analysis: Deep Type Inspection
Provides detailed symbol information: function signatures with parameter/return types, class structure with inheritance, interface contracts, type aliases with complexity management, and line-precise lookup. AI can generate type-safe refactorings and explain complex generic implementations.
3. Reference Finding: Cross-File Impact Analysis
Tracks symbol usage patterns across entire codebases: all locations where symbols are imported or used, surrounding code context, bounded results for large searches, and rename preview capabilities. AI assesses refactoring impact and identifies dead code candidates.
4. Compilation Diagnostics: Pre-Build Error Detection
Surface TypeScript errors without build execution: recursive directory scanning with glob patterns, severity filtering (errors/warnings/info), batch processing with configurable limits, and automatic tsconfig.json detection for accurate type checking. AI identifies type errors pre-build and validates generated code.
Solving Production-Scale Challenges
Technical problems encountered and how they were solved
Challenge: TypeScript Complexity
Conditional types, mapped types, and recursive types create explosion of edge cases. Solution: Leverage ts-morph's battle-tested abstractions supplemented with careful error handling and type string truncation for unbounded recursions. Strategic use of type checker only when necessary to avoid performance penalties.
Challenge: Performance at Enterprise Scale
10,000+ files make naive analysis approaches unusable (minutes per query). Solution: Three-layer optimization—caching to eliminate redundant parsing, parallelization to utilize multi-core processors, memory management to prevent crashes. Result: sub-second queries even on massive projects.
Challenge: Cross-Platform Compatibility
Different IDEs (Cursor, Claude Desktop, VSCode) with varying process models and communication channels. Solution: MCP protocol standardization with flexible transport layers (STDIO for local, HTTP+SSE for remote). Single codebase deployed via npx ensuring consistent behavior across platforms.
Challenge: Type Accuracy Without Compilation
Providing reliable type information without invoking tsc build pipeline. Solution: Direct interaction with TypeScript's language service layer via ts-morph Project API. Automatic tsconfig.json detection ensures analysis uses project-specific compiler options, matching production semantics without execution overhead.
Challenge: Error Resilience
Partially invalid TypeScript (mid-editing, incomplete refactors) must not crash the server. Solution: Defensive programming with try-catch boundaries around all ts-morph operations, graceful degradation returning partial results, and detailed error messages enabling AI guidance toward fixes.
Impact: Transforming AI Code Generation
How deep type information changes what's possible
Context-Aware Code Generation
- ✓ Generate imports automatically from existing patterns
- ✓ Suggest type-safe refactorings understanding symbol relationships
- ✓ Validate generated code against project conventions
Intelligent Refactoring
- ✓ Propose renames with full codebase impact analysis
- ✓ Identify breaking changes before implementation
- ✓ Suggest architectural improvements from dependency graphs
Proactive Bug Detection
- ✓ Surface type errors during conversation, not after build
- ✓ Identify null reference and type violation risks
- ✓ Catch interface contract violations pre-review
Reduced Manual Overhead
- ✓ AI self-services codebase information without developer intervention
- ✓ Faster iteration cycles with instant type information access
- ✓ Safer changes reducing compilation errors from AI code
Technology Stack
Core Libraries
- • ts-morph (v24+)
- • TypeScript (v5+)
- • @modelcontextprotocol/sdk
- • Node.js (v18+)
Distribution & Integration
- • npm (@r-mcp/static-analysis)
- • npx (zero-install deployment)
- • JSON-RPC 2.0 (standardized messaging)
- • STDIO/HTTP+SSE (flexible transport)
Integration Targets
- • Cursor IDE
- • Claude Desktop
- • Claude Code CLI
- • Any MCP-compatible client
Performance Features
- • Intelligent caching (AST in-memory)
- • Parallel processing (configurable limits)
- • Memory management (monitoring & eviction)
- • Error recovery (graceful degradation)
Adoption and Community Impact
Small but mighty: 12 stars from specialized technical communities
With 12 GitHub stars, the project has found its niche within specialized technical communities—advanced practitioners solving real production problems. This 'small but mighty' adoption pattern is characteristic of infrastructure tools: not mass-market metrics, but deep technical validation.
The project's impact extends beyond direct usage. It demonstrates best practices for MCP server implementation, sets performance benchmarks for static analysis tools in AI contexts, proves the viability of standardized protocols over custom integrations, and validates that ts-morph can power production-grade systems.
Conclusion: The Infrastructure Layer AI Needs
The MCP TypeScript Analyzer exemplifies engineering excellence in AI tooling. By combining strategic architectural decisions (ts-morph over raw APIs, MCP standardization), sophisticated performance engineering (caching, parallelization, memory management), and thoughtful feature design (four focused tools), the project solves genuine pain points in AI-assisted development.
Its true innovation lies in synthesis—bringing together mature technologies into a coherent, production-ready package. For development teams seeking to enhance AI assistants with deep codebase understanding, this tool represents a turnkey solution requiring zero custom integration.
As AI-assisted development matures, infrastructure like this will separate superficial chatbots from genuine productivity multipliers. This technical sophistication—balancing API complexity, performance constraints, and developer experience—provides a blueprint for the next generation of AI development tools.
Query Response Time
Files Supported
Core Analysis Tools
Platforms Supported
Production-grade static analysis for AI-assisted TypeScript development
MIT licensed, zero dependencies for installation, seamless IDE integration via Model Context Protocol
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