Mind Map MCP Server v1.22.0

Experimental Code Intelligence Platform - A Model Context Protocol (MCP) server that explores neuroscience-inspired approaches to software development analysis. This is an experimental research project featuring advanced query caching, associative learning patterns, context awareness, attention mechanisms, temporal knowledge modeling, and code analysis tools.

⚠️ Current Status: Experimental v1.22.0

🧪 This is experimental software under active development - Use for testing and research purposes. Features may change or be removed.

Features under development: Context-aware caching • Brain-inspired learning • Code pattern detection • Document analysis • Multi-language AST parsing • File ignore patterns • CI/CD automation • Memory optimization

🎯 Latest Update v1.22.0: Cross-Language API Detection - Comprehensive API endpoint detection across 12 programming languages. New Features: Detect REST APIs (Flask, Express, Spring Boot), GraphQL schemas, gRPC services, WebSocket endpoints, and WebAssembly modules. Language Support: Python, JavaScript/TypeScript, Java, Go, Rust, C++, C#, PHP, Ruby, Swift, Kotlin, and Scala. Intelligent Detection: Framework-specific patterns with confidence scoring for accurate API discovery. New dedicated MCP tool detect_cross_language_apis for direct API analysis.

⚠️ Important Disclaimer

This is experimental software developed for research and testing purposes. It explores various approaches to code analysis and project understanding using Model Context Protocol (MCP).

Before using:
- Expect bugs, incomplete features, and breaking changes
- Use in non-production environments only
- Backup your projects before extensive use
- Report issues and provide feedback to help improve the project

Use cases:
- Improving coding workflows with Claude Code - Enhanced project understanding and context
- Experimenting with MCP server development
- Testing code analysis and pattern detection approaches
- Research into alternative programming assistance tools
- Learning about AST parsing and project structure analysis

📦 Installation & Setup

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``bash


Install globally

npm install -g mind-map-mcp

Or install locally in your project

npm install mind-map-mcp
`

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#### 1. Automatic Setup (Recommended)

The easiest way to set up Mind Map MCP with Claude Code:

`bash


Run the automatic setup tool

npx mind-map-mcp init-claude-code

Or if installed globally

mind-map-mcp init-claude-code
`

This automatically:
- ✅ Detects your operating system and Claude installation
- ✅ Creates proper configuration files with correct paths
- ✅ Provides platform-specific setup instructions
- ✅ Includes verification commands and troubleshooting

#### 2. Manual Setup for Claude Desktop

If you prefer manual configuration, add this to your Claude Desktop config:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%/Claude/claude_desktop_config.json
Linux: ~/.config/claude-desktop/config.json

`json
{
"mcpServers": {
"mind-map-mcp": {
"command": "npx",
"args": ["mind-map-mcp"],
"env": {}
}
}
}
`

#### 3. Environment Variable Configuration

You can configure the MCP server to work with specific project directories using the MCP_PROJECT_ROOT environment variable:

`json
{
"mcpServers": {
"mind-map-mcp": {
"command": "npx",
"args": ["mind-map-mcp"],
"env": {
"MCP_PROJECT_ROOT": "/path/to/your/project"
}
}
}
}
`

What it does:
- Makes MCP scan and cache files in the specified project directory
- Creates .mindmap-cache folder in the target project
- Uses project-specific configuration and mind map data
- Allows working with multiple projects independently

Usage Examples:
`json
// For a specific project
"env": {
"MCP_PROJECT_ROOT": "/Users/yourname/projects/my-app"
}

// For a demo or test project
"env": {
"MCP_PROJECT_ROOT": "/Users/yourname/projects/demo-project"
}
`

Without this variable: MCP uses the current working directory where the MCP server was started.

#### 4. Verify Installation

After setup, restart Claude and verify the integration:

1. Check MCP Tools: In Claude, you should see 33 new MCP tools available
2. Test Basic Functionality: Try these commands in Claude:
`
Please scan the current project and show me the statistics.
`
3. Test Features: Try experimental features:
`
Please analyze the project architecture.
`

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Once installed, you can experiment with these commands in Claude:

#### Sample Workflow
`


Start with basic scanning:

Please scan the project and get initial statistics.

Explore analysis features:

Please analyze the project structure and suggest areas of focus.

Test learning features:

Please update the mind map with information about [task description].
`

#### 🔬 Experimental Analysis Features
`


Hebbian Learning - "Neurons that fire together, wire together"

Please show me the Hebbian learning statistics and top co-activation patterns.

Hierarchical Context - Multi-level awareness

Please get the hierarchical context stats and most relevant context items.

Attention System - Dynamic focus allocation

Please show the attention system statistics and allocate attention to important nodes.

Bi-temporal Knowledge - Valid vs Transaction time tracking

Please get bi-temporal statistics and create a context window for this session.

Pattern Prediction - Anticipatory intelligence

Please get pattern predictions and show emerging patterns.
`

#### Advanced Intelligence Features
`


Get architectural insights

Please analyze the project architecture and detect design patterns.

Find cross-language dependencies

Please detect cross-language dependencies in this polyglot project.

Get intelligent refactoring suggestions

Please generate multi-language refactoring suggestions focused on architecture.

Predict emerging code patterns

Please analyze and predict what code patterns are likely to emerge.
`

#### Development Tool Integration
`


Detect available tools

Please detect all development tools available in this project.

Run comprehensive analysis

Please run the full tool suite and provide aggregated results.

Get tool recommendations

Please recommend missing development tools that would benefit this project.
`

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✅ Installation: npm list -g mind-map-mcp shows the package
✅ Claude Integration: 33 MCP tools visible in Claude
✅ Basic Functionality: scan_project command works
✅ Advanced Features: Brain-inspired tools respond correctly
✅ Multi-language Support: AST analysis works for your languages

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Common Issues:

1. "No MCP tools visible" → Restart Claude after configuration
2. "Command not found" → Ensure npm global install path is in PATH
3. "Permission denied" → Run npm config get prefix and check permissions
4. "Server not responding" → Check Claude Desktop config file syntax

Get Help:
- 📖 Check QUICK_USAGE.md for workflow examples
- 📖 Read CLAUDE_CODE_SETUP.md for detailed setup
- 🐛 Report issues: https://github.com/nerfels/mind-map/issues

Features

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- Associative Memory System: Neural activation spreading across connected code concepts (50-70% relevance improvement)
- Context-Aware Query Caching: Intelligent caching with similarity matching (5-10x performance boost for repeated queries)
- Parallel Processing Engine: Chunked file analysis with worker pool orchestration (3-5x faster project scanning)
- Neuromorphic Query Patterns: Replaces linear search with brain-like associative activation networks
- Intelligent Cache Invalidation: Path-based selective cache clearing with LRU eviction and 100MB memory management
- Hebbian Learning System: Co-activation tracking with synaptic strengthening ("neurons that fire together, wire together")
- Inhibitory Learning: Failure avoidance through negative pattern recognition (30% reduction in repeated mistakes)
- Hierarchical Context Management: Multi-level context awareness (immediate, session, project, domain)
- Attention Mechanisms: Multi-modal attention fusion with cognitive load management (Miller's 7±2 rule)
- Bi-temporal Knowledge Model: Valid time vs transaction time tracking with complete audit trails
- Pattern Prediction Engine: Anticipates code patterns before they emerge using time series analysis and predictive forecasting

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- Variable Lazy Loading: Intelligent memory management with 40.3% reduction in variable node memory usage
- Smart Filtering: Only loads critical variables (exported, global, heavily-used >5 references) immediately
- Summary Node System: Creates single nodes containing lazy-loaded variable metadata for thousands of variables
- On-Demand Loading: 8ms average retrieval time for pattern-based variable queries
- Full Functionality Preservation: All variable querying capabilities maintained while dramatically reducing memory footprint
- Automatic Optimization: No configuration required - automatically detects important vs. lazily-loadable variables

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- Function Call Graph Construction: Complete call graph analysis with entry points, cycles, and depth calculation
- Constructor Call Detection: Accurate detection of class instantiation and constructor patterns
- Method Call Analysis: Comprehensive tracking of method invocations and chaining patterns
- Async/Await Pattern Recognition: Full support for asynchronous call pattern detection
- Recursion Detection: Automatic identification of recursive functions and call cycles
- Code Style Recognition: Comprehensive naming convention and style pattern analysis
- Complexity Calculation: Enhanced cyclomatic complexity with callback function and control flow analysis
- Cross-File Pattern Resolution: Advanced resolution of call patterns across multiple files

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- Automated Testing: Comprehensive test suite with multi-language AST validation
- Security Scanning: Automated vulnerability detection with npm audit integration
- Performance Monitoring: Continuous performance benchmarking with alert thresholds
- Code Quality Analysis: Bundle size monitoring, style analysis, and quality reporting
- Release Automation: Automated NPM publishing with GitHub release creation
- Maintenance Workflows: Dependency updates, health checks, and system monitoring
- Pull Request Validation: PR title validation, impact analysis, and comprehensive testing

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- Multi-Source Pattern Loading: Intelligent pattern merging from defaults, .gitignore, .mindmapignore, and custom configuration
- Real-Time Pattern Testing: Live pattern validation with performance metrics and file matching preview
- Pattern Analytics & Statistics: Comprehensive stats on pattern effectiveness, scan time reduction, and filtering efficiency
- Smart Default Patterns: 30 intelligent default patterns for common file types (node_modules, build artifacts, etc.)
- Configuration Management API: 3 new MCP tools for dynamic pattern updates and testing
- Developer-Friendly Interface: Familiar .gitignore syntax with enhanced capabilities and precedence rules
- Performance Optimization: 33% file filtering efficiency with 8-12ms pattern loading for improved scan performance
- Framework-Specific Patterns: Language and framework-specific ignore patterns (.pyc, .class, target/, dist/)

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- Multi-Language AST Analysis: Full parsing for 12 languages (TypeScript/JavaScript/Python/Java/Go/Rust/C++/PHP/C#/Ruby/Swift/Kotlin/Scala) with function/class extraction
- Dynamic Import Detection: Track runtime imports including import() calls, require() statements, template literals, and variable-based module loading for modern JavaScript/TypeScript applications
- Method Call Chain Analysis: Advanced call sequence tracking following A→B→C→D execution paths up to 10 levels deep with performance impact assessment and risk analysis
- Variable Usage Tracking: Comprehensive variable intelligence tracking declaration, usage, and modification patterns across files with lifecycle analysis and cross-module dependency detection
- Cross-Language Dependency Detection: Identifies API calls, FFI, microservices, and shared data patterns across languages
- Polyglot Project Analysis: Architectural style detection with multi-language recommendations
- Enterprise Framework Detection: React, Vue, Express, Django, Flask, Spring Boot, Laravel, ASP.NET, Rails, SwiftUI, Android, Akka, and 60+ more
- Architectural Pattern Detection: 7 pattern types with multi-language interoperability analysis
- Predictive Error Detection: Risk analysis system with language-specific pattern matching
- Intelligent Fix Suggestions: Context-aware recommendations with cross-language insights

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- 100+ Development Tools: Complete tooling ecosystem across 12 languages with intelligent detection
- Smart Tool Execution: Run tests, linters, formatters, and security scanners with issue parsing
- Intelligent Recommendations: Get suggestions for missing tools with installation commands
- Tool Suite Orchestration: Run multiple tools in parallel with aggregated results
- Issue Classification: Parse and categorize tool outputs for actionable insights
- Mind Map Integration: Store tool results as nodes/edges for learning and correlation

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- 25+ Framework Detection: Comprehensive framework analysis across 6 categories with confidence scoring
- Web Frameworks: React, Vue, Angular, Express, Django, Flask, Spring Boot, Next.js, Nuxt.js detection
- Mobile Frameworks: React Native, Flutter, Xamarin with platform-specific pattern analysis
- Desktop Frameworks: Electron, Tauri, Qt with configuration and build system detection
- Game Engines: Unity, Unreal Engine, Godot with project structure and script analysis
- ML/AI Frameworks: TensorFlow, PyTorch, scikit-learn with usage pattern detection
- Cloud Platforms: Docker, Kubernetes with manifest analysis and deployment patterns

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- Task Outcome Learning: Tracks success/failure patterns with confidence scoring
- Error Pattern Recognition: Categorizes errors and maps to successful solutions
- Cross-Session Intelligence: Maintains knowledge between Claude Code sessions
- Performance Learning: Adaptive optimization based on usage patterns
- Solution Effectiveness Tracking: Measures and improves recommendation quality

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- Cypher-like Graph Queries: Advanced querying with complex filtering and relationships
- Temporal Analysis: Code evolution tracking and change impact analysis
- Aggregate Analytics: Project insights, metrics, and trend analysis
- Semantic Search: Multi-factor relevance scoring with confidence weighting
- Saved Queries: Template system for common analysis patterns

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- Multi-Index Storage: Optimized indexing for type, path, name, confidence, framework, language
- LRU Caching: Memory optimization with intelligent cache management
- Performance Monitoring: Real-time operation timing and bottleneck detection
- Query Optimization: Execution planning and index hints for complex queries

Installation

`bash


Clone or create the project

npm install

Build the server

npm run build

Test the installation

npm start
`

Usage with Claude Code

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The easiest way to get started is to use the built-in initialization method:

`bash


After installing and building, run any Claude Code session and use:

mcp://mind-map-mcp/init_claude_code

Or for specific setups:

mcp://mind-map-mcp/init_claude_code?setup_type=desktop&platform=macos
mcp://mind-map-mcp/init_claude_code?setup_type=cli&platform=linux
`

This automatically generates:
- ✅ Platform-specific configuration files with correct paths
- ✅ Ready-to-copy JSON configurations for Claude Desktop/CLI
- ✅ Complete setup checklist with verification commands
- ✅ Quick start workflow with essential commands
- ✅ Troubleshooting guide for common issues
- ✅ CLAUDE.md template for project-specific instructions

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If you prefer manual setup, add this server to your Claude Code MCP configuration:

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Edit your Claude Desktop config file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%/Claude/claude_desktop_config.json

`json
{
"mcpServers": {
"mind-map-mcp": {
"command": "node",
"args": ["/path/to/mind-map-mcp/dist/index.js"],
"env": {}
}
}
}
`

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`bash


Start the server directly (for testing)

npm start

Or use the built binary

node dist/index.js
`

Available MCP Tools (27 Total)

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- scan_project: Initial project analysis with AST parsing and pattern detection
- query_mindmap: Semantic search with confidence scoring and relevance ranking
- update_mindmap: Learning system for task outcomes and error patterns
- suggest_exploration: Intelligent file/function recommendations
- get_context: Project overview and contextual information
- get_stats: Comprehensive project statistics and metrics

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- predict_errors: Risk analysis and error prediction based on patterns
- suggest_fixes: Context-aware fix recommendations for error patterns
- analyze_architecture: Detect architectural patterns and design insights
- advanced_query: Cypher-like graph queries with complex filtering
- temporal_query: Code evolution and change impact analysis
- aggregate_query: Project metrics, insights, and trend analysis

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- get_performance: Real-time performance monitoring and bottleneck detection
- get_cache_stats: Query cache performance metrics and memory usage statistics
- clear_cache: Intelligent cache invalidation with selective path-based clearing
- save_query: Save and manage reusable query templates
- execute_saved_query: Run saved queries with parameter substitution
- get_insights: Comprehensive project insights with actionable recommendations

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- detect_cross_language_deps: Identify cross-language dependencies and communication patterns
- analyze_polyglot_project: Analyze multi-language project structure and architecture
- generate_multi_language_refactorings: Generate refactoring suggestions for polyglot codebases

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- detect_project_tooling: Detect available development tools across all languages in the project
- run_language_tool: Execute specific development tools with intelligent issue parsing
- get_tooling_recommendations: Get intelligent recommendations for missing development tools
- run_tool_suite: Run multiple development tools in parallel with aggregated results

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- detect_enhanced_frameworks: Comprehensive framework detection across web, mobile, desktop, game, ML/AI, and cloud categories
- get_framework_recommendations: Get intelligent recommendations based on detected frameworks and project patterns

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`json
{
"name": "scan_project",
"arguments": {
"force_rescan": false,
"include_analysis": true,
"ast_analysis": true
}
}
`

`json
{
"name": "advanced_query",
"arguments": {
"query": "MATCH (f:file)-[:contains]->(func:function) WHERE func.name CONTAINS 'auth' RETURN f.path, func.name",
"limit": 10
}
}
`

`json
{
"name": "predict_errors",
"arguments": {
"file_path": "src/auth/login.ts",
"context": "implementing OAuth integration"
}
}
`

How It Works

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The server performs comprehensive scanning and creates nodes for:
- Files & Directories: Complete project structure with metadata
- AST Elements: Functions, classes, interfaces, imports/exports with full signatures
- Architectural Patterns: 7 pattern types with confidence scoring
- Framework Detection: React, Vue, Express, Django, Flask, pandas, NumPy, etc.
- Error Patterns: Historical error categorization and solution mapping

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Full support for 12 major programming languages:

Original Languages:
- TypeScript/JavaScript: Complete AST with function/class extraction via TypeScript compiler API
- Python: Full AST parsing with subprocess execution for functions, classes, decorators
- Java: Complete AST parsing with java-parser for classes, methods, annotations, Spring Boot detection
- Go: Go AST parsing with struct/interface/function extraction and framework detection
- Rust: Rust AST analysis with struct/trait/impl extraction and crate dependency mapping
- C/C++: C++ parsing with class/function/template extraction and build system analysis

NEW Languages (v1.1.4):
- PHP: Complete AST with class/method extraction and Laravel/Symfony framework detection
- C#: Full AST parsing with ASP.NET/Entity Framework detection and namespace analysis
- Ruby: Ruby AST with class/method extraction and Rails/Sinatra framework detection
- Swift: Swift AST parsing with UIKit/SwiftUI framework detection and protocol analysis
- Kotlin: Kotlin AST with Android/Compose framework detection and coroutine analysis
- Scala: Scala AST parsing with Akka/Play framework detection and trait analysis

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Revolutionary neuromorphic computing principles applied to code intelligence:
- Associative Memory Networks: Neural activation spreading replaces linear search (50-70% relevance improvement)
- Context-Aware Caching: Intelligent similarity matching with LRU eviction (5-10x performance boost)
- Parallel Processing Engine: Worker pool orchestration with chunked analysis (3-5x faster scanning)
- Neuromorphic Query Patterns: Brain-like activation across connected code concepts
- Intelligent Memory Management: 100MB cache with path-based invalidation and exponential decay
- Episodic Memory System (NEW v1.1.4): Store and retrieve programming experiences with 77.1% similarity matching accuracy and experience-based suggestions with 81.1% confidence

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As you use Claude Code, the server:
- Tracks Task Outcomes: Success/failure patterns with confidence adjustment
- Maps Error Solutions: Categorizes errors and associates with successful fixes
- Builds Pattern Recognition: Framework usage, naming conventions, architectural insights
- Optimizes Performance: LRU caching and multi-index storage for faster queries

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Advanced querying capabilities include:
- Cypher-like Syntax: Complex graph traversal with filtering and aggregation
- Semantic Search: Multi-factor relevance scoring (exact, path, confidence, recency)
- Temporal Analysis: Code evolution tracking and change impact assessment
- Predictive Analytics: Error risk assessment and fix suggestion engine

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All learning persists locally with:
- Graph Database: Nodes, edges, and relationship storage in JSON format
- Performance Monitoring: Operation timing and bottleneck detection
- Query Optimization: Execution planning and index hints
- Cache Management: LRU eviction and intelligent memory optimization

Data Storage

The mind map data is stored locally in your project directory:

`
your-project/
├── .mindmap-cache/
│ └── mindmap.json # Serialized knowledge graph
└── ... (your project files)
`

Privacy & Security

- Local-Only: All data stays on your machine
- No Network: No external API calls or data transmission
- Configurable: Choose what gets tracked
- Transparent: All data stored in readable JSON format

Development

`bash


Development with auto-rebuild

npm run dev

Type checking

npm run type-check

Linting

npm run lint

Testing

npm test
``

Architecture

The server consists of several key components:

- MindMapEngine: Core intelligence and query processing
- MindMapStorage: Graph database operations and persistence
- FileScanner: Project analysis and file classification
- MCP Server: Protocol implementation and tool handling

Roadmap

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- Core MCP Server: 25 tools with stdio transport
- Multi-Language AST Analysis: 12 languages (TypeScript/JavaScript/Python/Java/Go/Rust/C++/PHP/C#/Ruby/Swift/Kotlin/Scala) with comprehensive parsing
- Cross-Language Intelligence: Dependency detection, polyglot analysis, multi-language refactoring
- Development Tooling Integration: 100+ tools across 12 languages with intelligent execution and parsing
- Enhanced Framework Detection: 60+ frameworks across 6 categories (web, mobile, desktop, game, ML/AI, cloud)
- Advanced Intelligence: Predictive errors, fix suggestions, architectural patterns, risk analysis
- Enterprise Querying: Cypher-like queries, temporal analysis, aggregates, saved queries
- Performance Systems: Multi-index storage, LRU caching, monitoring, insights

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- Phase 5.1: ✅ Python AST support with Flask/Django detection
- Phase 5.2: ✅ Java AST support with Spring Boot/Maven/Gradle detection
- Phase 5.3: ✅ Go AST support with Gin/Echo framework detection
- Phase 5.4: ✅ Rust AST support with Actix/Tokio/Serde detection
- Phase 5.5: ✅ C/C++ AST support with Qt/Boost/CMake detection
- Phase 5.7: ✅ Multi-Language Intelligence with cross-language dependency detection
- Phase 5.8: ✅ Language-Specific Tooling Integration with 80+ development tools
- Phase 5.9: ✅ Enhanced Framework Detection across 6 categories with 25+ frameworks

📋 Version History

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- ✅ Enhanced Query System: Comprehensive improvements to core query functionality
- ✅ Multi-Word Query Support: Perfect handling of queries like "mind map", "pattern analysis"
- ✅ Semantic Language Mapping: "typescript" finds .ts files, "javascript" finds .js files
- ✅ Exact File Path Matching: Direct file queries like "src/core/MindMapEngine.ts"
- ✅ Advanced CamelCase Handling: Full support for camelCase, PascalCase, and mixed case queries
- ✅ Improved Temporal Queries: Enhanced time-based analysis with evolution metrics
- ✅ Better Advanced Query Engine: Cypher-like syntax improvements for complex graph queries
- ✅ Enhanced Aggregate Queries: Improved grouping and field extraction for statistical analysis

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- ✅ Fixed Java Code Structure Recognition: Resolved Java class/method extraction issues
- ✅ Enhanced Java AST Parsing: Complete Java file code intelligence with proper node separation

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- ✅ Advanced Call Pattern Analysis: Complete function call graph construction with 100% test success rate
- ✅ Constructor Call Detection: Accurate class instantiation and constructor pattern recognition
- ✅ Enhanced Complexity Calculation: Improved cyclomatic complexity with callback functions and control flow
- ✅ Code Style Recognition: Comprehensive naming convention and style pattern analysis (camelCase, PascalCase, snake_case)
- ✅ CI/CD Pipeline Infrastructure: Complete automation with testing, security scanning, and performance monitoring
- ✅ Release Automation: Automated NPM publishing with GitHub release workflows
- ✅ Pull Request Validation: Comprehensive PR checks with impact analysis and quality gates

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- ✅ 12 Programming Languages + Episodic Memory System
- ✅ Added comprehensive support for PHP, C#, Ruby, Swift, Kotlin, and Scala
- ✅ Complete AST parsing and framework detection
- ✅ Brain-inspired episodic memory with 77.1% similarity matching accuracy

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- ✅ Multi-modal confidence fusion and brain-inspired intelligence platform
- ✅ Performance optimization and cache improvements
- ✅ Enhanced query description with semantic search capabilities

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- ✅ Enhanced README with comprehensive Claude Code integration guide
- ✅ Added step-by-step installation instructions
- ✅ Included usage examples and workflow guides
- ✅ Added troubleshooting section and verification checklist

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- ✅ Complete brain-inspired intelligence system
- ✅ Enterprise scalability and user customization
- ✅ Organized test suite with proper structure
- ✅ 33 advanced MCP tools fully functional
- ✅ Multi-language support (6 languages)
- ✅ 80+ development tools integration

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- Visual Interface: Mind map visualization and exploration
- Team Sharing: Collaborative knowledge base
- IDE Integrations: VS Code, IntelliJ, Vim plugins
- Advanced ML: Enhanced neural pattern recognition

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

MIT License - see LICENSE file for details.

Support

- Report issues on GitHub
- Check the troubleshooting guide in docs/
- Review the API documentation for integration details