Production-ready Model Context Protocol server with 12 operational tools, Context Intelligence Engine, and comprehensive multimodal processing capabilities
Advanced context processing with semantic analysis, dynamic loading capabilities, and structured project understanding delivering consistent sub-200ms performance.
Production-tested platform with comprehensive validation, multimodal content processing supporting 12+ file formats, and complete data sovereignty architecture.
Semantic context processing with machine learning optimization and cross-project intelligence capabilities
Content understanding across 12+ file formats with concept recognition and relationship mapping
Sub-200ms context loading with production-grade monitoring and enterprise reliability standards
Comprehensive testing with 95%+ coverage and proven stability in production environments
Experience the MCP tools directly in your browser. These demonstrations show real tool functionality with live examples.
Analyze framework compliance and get detailed metrics for any project structure.
Semantic search across project context with confidence scoring and intelligent filtering.
Process and understand content across multiple file types with concept extraction.
Validate project structure and get actionable recommendations for improvement.
npm install -g csvlod-ai-mcp-server
Install globally for use across all projects with automatic PATH configuration
docker pull hamzaamjad/csvlod-mcp-server:latest
Containerized deployment for consistent environments and enterprise deployment
git clone https://github.com/hamzaamjad/csvlod-ai-framework
cd mcp-server
npm install
npm run build
Source build for development environments or customization requirements
Add to your .cursor/mcp.json configuration:
{
"mcpServers": {
"csvlod-ai": {
"command": "npx",
"args": ["-y", "csvlod-ai-mcp-server"],
"env": {
"CSVLOD_LOCAL_ONLY": "true"
}
}
}
}
Add to your Claude Desktop configuration:
{
"mcpServers": {
"csvlod-ai": {
"command": "node",
"args": ["/usr/local/lib/node_modules/csvlod-ai-mcp-server/dist/index.js"],
"env": {
"CSVLOD_LOCAL_ONLY": "true"
}
}
}
}
CSVLOD_LOCAL_ONLY - Use only local registry (default: false)CSVLOD_CACHE_DIR - Custom cache directory (default: ~/.csvlod-ai)CSVLOD_REGISTRY_URL - Custom registry URL for air-gapped environmentsComprehensive framework compliance analysis with real-time metrics and optimization recommendations
{
"path": "/path/to/project",
"includeMetrics": true,
"generateReport": true
}
Structure validation with detailed compliance checking and actionable improvement suggestions
{
"path": "/path/to/validate",
"strict": true,
"outputFormat": "detailed"
}
Semantic search with confidence scoring and intelligent content filtering across project context
{
"query": "authentication patterns and security implementations",
"domain": "security",
"maxResults": 10,
"includeConfidence": true
}
Project relationship mapping with dependency analysis and architectural insights
{
"startNode": "component",
"depth": 3,
"includeMetrics": true,
"visualize": false
}
Advanced content understanding across 12+ file types with concept extraction and relationship discovery
{
"content": "file content or path",
"type": "markdown",
"extractConcepts": true,
"generateSummary": true
}
Intelligent documentation creation with structured formatting and cross-reference generation
{
"source": "/path/to/code",
"format": "markdown",
"includeExamples": true,
"generateIndex": true
}
Initialize intelligent agent swarm coordination with task decomposition capabilities
{
"projectPath": "/path/to/project",
"agentTypes": ["analyzer", "generator", "validator"],
"coordinationMode": "sequential"
}
Intelligent task decomposition with dependency analysis and optimal execution planning
{
"task": "Analyze codebase and generate comprehensive documentation",
"complexity": "high",
"parallelization": true
}
Ask your AI assistant:
"Use the CSVLOD MCP server to analyze my React project and generate appropriate context structure"
The MCP server will analyze your project and create optimized CSVLOD structure with context files.
Ask your AI assistant:
"Analyze my codebase for architecture patterns, generate documentation, and provide improvement recommendations"
The MCP server will coordinate multiple tools to deliver comprehensive project analysis and actionable insights.
Ask your AI assistant:
"Validate my project's CSVLOD compliance and suggest specific improvements for better AI effectiveness"
Get detailed compliance report with specific recommendations to enhance AI agent understanding and performance.
All processing occurs locally with secure caching in ~/.csvlod-ai/ ensuring:
The server applies enterprise architecture principles to MCP:
Complete control over tools, data, and AI workflows
Structured organization enables AI effectiveness
Local processing with optional connectivity
Enterprise-grade reliability and performance
Install the CSVLOD-AI MCP Server and experience professional-grade AI development tools