enhanced-context-mcp

premkalyan/enhanced-context-mcp

3.2

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The Enhanced Context MCP Server v2.0.0 is a serverless solution for intelligent context management, designed for deployment on Vercel.

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Enhanced Context MCP Server v2.0.0

Serverless Enhanced Context MCP Server with Project Registry Integration

Overview

The Enhanced Context MCP Server provides intelligent context loading for AI agents, automatically selecting appropriate templates, contexts, and agent profiles based on query type. This is a complete TypeScript rewrite of the original server, designed for serverless deployment on Vercel.

Features

  • 🎯 Automatic Agent Selection - Intelligently selects the right VISHKAR agent based on query type
  • 📝 Template Loading - Loads appropriate templates from VISHKAR repository
  • 🔧 Project-Specific Rules - Supports project-specific configuration and rules
  • 💾 Vercel Blob Storage - Scalable file storage for contexts and templates
  • ⚡ Vercel KV Caching - Fast response times with Redis-backed caching
  • 🔒 Secure Authentication - Integration with Project Registry for credential management
  • 🚀 Serverless Architecture - Fully serverless deployment on Vercel

Architecture

Clean Architecture Layers

app/api/              # Presentation Layer (API Routes)
lib/
  ├── services/       # Application Layer (Business Logic)
  ├── domain/         # Domain Layer (Entities)
  ├── infrastructure/ # Infrastructure Layer (Storage, Auth, Monitoring)
  ├── utils/          # Utilities (Validation, Security)
  └── config/         # Configuration Management

Storage Abstraction

The server uses a storage adapter pattern that switches between:

  • Development: File system storage (~/.wama)
  • Production: Vercel Blob storage with KV caching

Available Tools

1. load_enhanced_context

Load global WAMA contexts, templates, and project-specific rules based on query type.

Query Types:

  • story - User story and epic creation
  • testing - Test planning and strategy
  • security - Security reviews and audits
  • architecture - Architecture design and documentation
  • pr-review - Pull request reviews
  • browser-testing - Browser automation testing
  • project-planning - Project planning and management
  • story-breakdown - Breaking down epics into stories
  • documentation - Technical documentation
  • flow-diagrams - Flow and sequence diagrams
  • infrastructure - Infrastructure as code and cloud architecture

2. list_vishkar_agents

List all available VISHKAR agent profiles.

3. load_vishkar_agent

Load complete VISHKAR agent profile by ID.

4. validate_vishkar_agent_profile

Validate VISHKAR agent profile format and completeness.

5. refresh_agent_cache

Clear cached agent profiles and reload from storage.

6. update_agent

Update existing agent configurations with learning improvements.

API Endpoints

GET /api/health

Health check endpoint.

POST /api/mcp

Main MCP endpoint for tool calls.

Request:

{
  "tool": "load_enhanced_context",
  "arguments": {
    "query_type": "story",
    "project_path": "/optional/path/to/project"
  }
}

Headers:

X-API-Key: pk_your_api_key
Content-Type: application/json

GET /api/mcp

List available tools.

Development

Prerequisites

  • Node.js 20+
  • npm
  • Vercel account (for deployment)

Local Setup

# Install dependencies
npm install

# Copy environment variables
cp .env.example .env.local

# Run development server
npm run dev

The server will be available at http://localhost:3000

Local Testing

For local development, the server uses the file system storage adapter which reads from ~/.wama:

~/.wama/
├── contexts/      # Context files (.mdc)
├── templates/     # Template files (.md)
└── agents/        # Agent profile files (.md)

Building

npm run build

Linting

npm run lint

Deployment

Vercel Setup

  1. Install Vercel CLI:
npm i -g vercel
  1. Login to Vercel:
vercel login
  1. Deploy:
vercel --prod

Environment Variables

Configure in Vercel Dashboard:

  • PROJECT_REGISTRY_URL - URL of the Project Registry
  • BLOB_READ_WRITE_TOKEN - Automatically provided by Vercel Blob
  • KV_REST_API_URL - Automatically provided by Vercel KV
  • KV_REST_API_TOKEN - Automatically provided by Vercel KV

Vercel Blob Setup

  1. Go to Vercel Dashboard → Your Project → Storage
  2. Create a new Blob Store
  3. Environment variables are automatically injected

Vercel KV Setup

  1. Go to Vercel Dashboard → Your Project → Storage
  2. Create a new KV Store
  3. Environment variables are automatically injected

Integration with Project Registry

This server integrates with the Project Registry for centralized credential management:

  1. Register your project in the Project Registry
  2. Store any necessary credentials (encrypted with AES-256-GCM)
  3. Use the provided API key in the X-API-Key header
  4. The server validates credentials via the Project Registry

Security

Authentication

  • API key authentication via X-API-Key header
  • Integration with Project Registry for validation
  • Timing-safe comparison for token validation

Path Traversal Protection

  • All file paths are validated before access
  • No access to files outside allowed directories
  • Normalized path resolution

Storage Security

  • Vercel Blob: Public read access, authenticated write
  • Vercel KV: Encrypted at rest
  • No sensitive data in client-side code

Performance

  • Caching: Agent profiles cached in Vercel KV (1 hour TTL)
  • Storage: Vercel Blob with CDN edge caching
  • Cold Start: < 500ms typical response time
  • Concurrent: Handles 1000+ concurrent requests

Migration from v1.0

This is a complete rewrite with:

  • TypeScript throughout
  • Clean architecture
  • Serverless-first design
  • Vercel Blob/KV storage
  • Improved error handling
  • Better testability

License

MIT

Support

For issues and feature requests, please use the GitHub issue tracker.