| name | token-efficient-mcp-template |
| description | This skill should be used when creating token-efficient hook systems and MCP server architectures for web applications. Use for React/FastAPI, Django, Node.js projects that need smart caching, progressive context loading, and 95%+ token reduction. Essential for projects requiring monitoring, debugging, data analysis, and system health tools. |
Token-Efficient MCP & Hook System Template
Overview
Transform any web application into a token-efficient ecosystem with smart MCP server tools and sophisticated hook systems. Provides 95%+ token reduction through intelligent caching, progressive context loading, and optimized tool discovery patterns.
Quick Start
Initialize new project ecosystem:
# Run the initialization script
python scripts/init_template.py --project-type {react|fastapi|django|node} --name my-project
Generate specific components:
- Hook system:
python scripts/generate_hooks.py --type {pre-tool|context|session} - MCP server:
python scripts/setup_mcp_server.py --categories {logs,system,database} - Caching:
python scripts/configure_caching.py --strategy {smart|progressive|differential}
Core Capabilities
1. Hook System Generation
Create sophisticated hook systems that provide real-time tool guidance and token efficiency nudges.
Pre-Tool-Use Hooks:
- Analyze tool usage patterns before execution
- Suggest token-efficient alternatives (95-99% savings)
- Validate tool parameters and security constraints
- Provide context-aware recommendations
Context Injection Hooks:
- Progressive context loading (summary → targeted → full)
- Session state management and tracking
- Dependency resolution and caching
- Background refresh mechanisms
Session Management Hooks:
- Timeout protection and resource limits
- Background task coordination
- Health monitoring and validation
- Graceful error handling and recovery
2. MCP Server Architecture
Bootstrap production-ready MCP servers with modular tool categories and intelligent caching.
Tool Categories:
- logs: Error pattern analysis, log aggregation, troubleshooting
- system: Health monitoring, performance metrics, status checks
- database: Query optimization, connection management, backup verification
- performance: Real-time monitoring, resource tracking, bottleneck detection
- execution: Sandboxed code execution, data transformation, analysis
- optimization: Token efficiency, cache management, progressive disclosure
Built-in Features:
- Smart caching with configurable TTLs
- Progressive disclosure patterns
- Background refresh strategies
- Token usage optimization
- Security sandboxing
3. Token Efficiency Framework
Implement proven patterns for dramatic token reduction without sacrificing functionality.
Smart Caching Strategies:
- Differential analysis (99% token reduction)
- Context-aware summarization
- Intelligent cache invalidation
- Background refresh for stale data
Progressive Context Loading:
- Summary mode (150 tokens)
- Targeted mode (800 tokens)
- Full context (complete file/data)
- Automatic level selection based on user intent
Optimization Patterns:
- Query result aggregation
- Batch processing for multiple operations
- Compression of repetitive data
- Smart prefetching based on usage patterns
Project Templates
React/FastAPI Template
python scripts/init_template.py --project-type react-fastapi --name my-app
- Frontend-backend tool integration
- WebSocket real-time updates
- API endpoint monitoring
- Component-level performance tracking
Django Analytics Template
python scripts/init_template.py --project-type django-analytics --name dashboard
- Database query optimization
- Admin panel integration
- User activity tracking
- Report generation tools
Node.js Microservices Template
python scripts/init_template.py --project-type node-microservices --name services
- Service health monitoring
- Inter-service communication tracking
- Load balancing metrics
- Distributed tracing tools
Resources
scripts/
Executable automation scripts for template generation and project setup.
Core Scripts:
init_template.py- Initialize complete project ecosystemgenerate_hooks.py- Generate hook system componentssetup_mcp_server.py- Create MCP server with tool categoriesconfigure_caching.py- Set up token efficiency strategieshealth_check.py- Validate system connectivity and health
Usage Example:
# Initialize React/FastAPI project with full ecosystem
python scripts/init_template.py \
--project-type react-fastapi \
--name my-ecommerce-app \
--include-mcp-server \
--include-hooks \
--caching-strategy smart
references/
Comprehensive documentation and implementation guides.
Key References:
hook_patterns.md- Hook implementation best practices and patternsmcp_design_guide.md- MCP server architecture and design principlestoken_optimization.md- Caching strategies and token efficiency techniquessecurity_guide.md- Sandbox configurations and security best practicesintegration_examples.md- Detailed integration examples for different stacks
Reference Usage:
- Load specific references when implementing custom components
- Consult design guides for architectural decisions
- Use examples as templates for project-specific customizations
assets/
Template files and configurations for different project types.
Template Categories:
hook_templates/- Hook system templates for different environmentsmcp_templates/- MCP server tool definitions and schemasconfig_templates/- YAML configurations for different stacksdocker_templates/- Container configurations and deployment scripts
Asset Usage:
- Copy templates directly to new projects
- Customize configurations for specific requirements
- Use as starting points for custom implementations
Implementation Workflow
Phase 1: Project Analysis
- Identify project type and requirements
- Determine needed tool categories
- Select caching and optimization strategies
- Configure security and performance parameters
Phase 2: Template Generation
- Run initialization script with project parameters
- Generate hook system components
- Set up MCP server with selected tools
- Configure caching and optimization strategies
Phase 3: Customization
- Adapt templates to project-specific needs
- Configure tool parameters and security settings
- Set up monitoring and health checks
- Test integration and validate functionality
Phase 4: Deployment
- Configure deployment environments
- Set up monitoring and alerting
- Validate performance and token efficiency
- Document customizations and usage patterns
Best Practices
Token Efficiency:
- Always use progressive context loading for large files
- Implement smart caching with appropriate TTLs
- Use differential analysis for change detection
- Batch operations when possible
Security:
- Configure sandboxing for code execution
- Implement proper authentication and authorization
- Use environment-specific configurations
- Regular security audits and updates
Performance:
- Monitor token usage and optimize patterns
- Use background refresh for stale data
- Implement proper error handling and recovery
- Regular performance validation and tuning
This skill transforms the sophisticated patterns from production systems into reusable templates, enabling any developer to create token-efficient tool ecosystems with minimal effort and maximum impact.