Optivise

This Package is Under Development. Please allow me some more days. Will bring something awesome soon.

Optivise is an intelligent MCP (Model Context Protocol) tool that enhances AI-assisted Optimizely development. It analyzes developer prompts for Optimizely relevance and provides curated, contextual information to LLMs.

Features (Current)

• 5 Specialized MCP Tools:

  • optidev_context_analyzer: Enhanced context analysis with product detection

  • optidev_implementation_guide: Jira ticket analysis and implementation planning

  • optidev_debug_helper: Intelligent bug analysis and resolution

  • optidev_code_analyzer: Real-time code analysis and optimization

  • optidev_project_helper: Project setup, migration, and configuration assistance

• AI-Powered Capabilities (Optional):

  • OpenAI integration for embeddings and semantic search (optional)

  • ChromaDB vector database for documentation search (optional)

  • Deterministic relevance scoring with evidence and rules

  • Graceful fallbacks when AI features unavailable

• Observability & Safety:

  • Structured JSON logs on stderr with correlation IDs; MCP stdout clean

  • Log redaction, block sanitization (scripts/iframes/JS/data URIs), content size ceilings

  • Diagnostics with per-stage timings and relevance breakdown

Related MCP server: LLM Optimizer

Installation

# Install Optivise globally
npm install -g optivise

# Verify installation
optivise version

IDE Configuration

For Cursor IDE:

Create or update .cursor/mcp.json in your project:

{
  "mcpServers": {
    "optivise": {
      "command": "npx",
      "args": ["optivise-mcp"]
    }
  }
}

For VS Code:

Add to your VS Code settings:

{
  "mcp.servers": [
    {
      "name": "optivise",
      "command": "npx",
      "args": ["optivise-mcp"]
    }
  ]
}

Usage

@optidev_context_analyzer "How do I implement a custom handler chain in Optimizely Commerce?"

AI Enhancement (Optional)

To enable AI-powered features:

{
  "mcpServers": {
    "optivise": {
      "command": "npx",
      "args": ["optivise-mcp"],
      "env": {
        "OPENAI_API_KEY": "your-api-key-here"
      }
    }
  }
}

CLI Utilities

# Propose a consolidated .cursorrules from discovered rules (prints JSON with diff)
optivise-rules propose /path/to/project

# Write the proposed .cursorrules to the project root
optivise-rules propose /path/to/project --write

# Print version and service diagnostics (AI/Chroma/doc-sync availability)
optivise-diag

# Query local HTTP server health or readiness
optivise-health                   # defaults to http://localhost:3007/health
optivise-health --ready           # queries http://localhost:3007/ready
optivise-health --url=http://host:port/ready

Environment Variables

• LOG_LEVEL: error|warn|info|debug (default: info)

• OPTIVISE_MODE: mcp|server (default: mcp)

• MAX_BLOCK_CHARS: max characters per context block (default: 5000)

• MAX_TOTAL_TOKENS: hard safety ceiling for context tokens (default: 4000)

• OPENAI_API_KEY: optional, enables AI-powered features

• CORS_ALLOW_ORIGINS: comma-separated allowed origins for HTTP server (default: *)

• REQUEST_TIMEOUT_MS: per-request timeout for /analyze (default: 15000)

• AUDIT_API_KEY: enables protected GET /audit endpoint when OPTIVISE_AUDIT=true

• OPTIVISE_AUDIT: set to 'true' to enable in-memory audit trail (requires AUDIT_API_KEY for access)

Troubleshooting

MCP Server Not Connecting

• Verify Node.js version: Ensure Node.js >= 18.0.0

• Restart IDE after configuration changes

• Check logs: Set LOG_LEVEL=debug for detailed logs

Tools Not Available

• Verify configuration: Ensure optivise-mcp is correctly referenced

• Test connection: npx @modelcontextprotocol/inspector npx optivise-mcp

Windows Path Issues

Use forward slashes or double backslashes in JSON:

"args": ["optivise-mcp"]

Render Deployment (Example)

See render.yaml for a minimal configuration:

services:
  - type: web
    name: optivise
    env: node
    plan: free
    buildCommand: npm install && npm run build
    startCommand: npm start
    envVars:
      - key: NODE_ENV
        value: production
      - key: OPTIVISE_MODE
        value: server
      - key: OPTIDEV_DEBUG
        value: false
      - key: CORS_ALLOW_ORIGINS
        value: https://yourdomain.com
    healthCheckPath: /health

After deployment:

• GET /health for liveness

• GET /ready for feature matrix + circuit states (OpenAI/Chroma)

• Use optivise-health locally to check http://localhost:3007/health

Audit Trail (Opt-in)

• Enable: set OPTIVISE_AUDIT=true and set a strong AUDIT_API_KEY.

• Fetch recent events:

curl -H "Authorization: Bearer $AUDIT_API_KEY" http://localhost:3007/audit | jq

Security & Privacy (Current)

• Log redaction (API keys/tokens/passwords), correlation IDs, MCP stdout kept clean

• Output sanitization and size bounds in formatter; relevance-aware truncation

• Opt-in in-memory audit trail for tool invocations (protected endpoint)

• Circuit breakers and backoff for AI/Chroma integrations; CI npm audit + CycloneDX SBOM

Planned (not yet implemented): stronger PII detection, allow-listed HTML sanitization, signed releases, and comprehensive policy scans (e.g., OSV).

Use Cases & Examples

For Individual Developers

@optidev_implementation_guide "Implement customer loyalty points system"
@optidev_debug_helper "Cart total calculation incorrect after discount applied"
@optidev_code_analyzer "Review this handler for performance optimization"

For Development Teams

@optidev_project_helper "Setup new Commerce + CMS integrated project"
@optidev_context_analyzer "Best practices for integrating Commerce with CMS"

Data Flow Diagram

---
config:
  theme: neo-dark
---
flowchart TD
    User[User/Developer] -->|Prompt| IDE[IDE/CLI Interface]
    IDE -->|Request| MCP[MCP Server]
    MCP -->|Initialize| CAE[Context Analysis Engine]
    MCP -->|Initialize| Tools[Specialized Tools]
    MCP -->|Initialize| AI[AI Services]
    
    subgraph "Context Analysis Flow"
        CAE -->|Analyze Prompt| PA[Prompt Analyzer]
        PA -->|Relevance Score & Intent| CAE
        
        CAE -->|Detect Products| PDS[Product Detection Service]
        PDS -->|Product Context| CAE
        
        CAE -->|Analyze Rules| RIS[Rule Intelligence Service]
        RIS -->|Rule Analysis| CAE
        
        CAE -->|Fetch Documentation| DS[Documentation Service]
        
        DS -->|Basic Docs| DOC[Documentation Sources]
        DS -->|Vector Search| CDB[ChromaDB Service]
        CDB -->|AI-Enhanced Search| OAI[OpenAI Client]
        OAI -->|Embeddings| CDB
        
        DS -->|Documentation Content| CAE
    end
    
    subgraph "AI Services"
        AKDS[API Key Detector] -->|Detect Keys| OAI
        AKDS -->|Detect Keys| CDB
        DSS[Documentation Sync Service] -->|Sync| CDB
    end
    
    subgraph "Specialized Tools"
        Tools -->|Implementation Guide| IGT[Implementation Guide Tool]
        Tools -->|Debug Helper| DHT[Debug Helper Tool]
        Tools -->|Code Analyzer| CAT[Code Analyzer Tool]
        Tools -->|Project Helper| PHT[Project Helper Tool]
        
        IGT & DHT & CAT & PHT -->|Use| CAE
    end
    
    CAE -->|Curated Context| MCP
    MCP -->|Response| IDE
    IDE -->|Enhanced Response| User

Documentation & Support

• IDE Agent Integration: How to assemble prompts and previews in Cursor/VS Code

• Developer Guide: How to develop, run, test, and integrate Optivise locally

• Architecture Diagrams: System architecture and data flows

• Product Requirements: Complete product requirements and features

• Software Requirements: Technical specifications and interfaces

• Project Plan: Development roadmap and timeline

• Vision and Scope: Project vision and objectives

• Todo List: Implementation status and completed features

• Claude Integration: Guidance for Claude AI when working with this codebase

Contact

• Issues: GitHub Issues

• Support: Enterprise Support