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Why AI coding assistants drive you crazy

You start excited about building a feature, but watch the AI slowly lose track of what you're building...

Chaotic AI Planning

Current Reality
1
"I want user auth"

AI tries to build everything at once

2
"20 tasks in one response"

Database, routes, middleware, frontend, tests...

3
"Task 8 forgot task 3"

Conflicts with earlier code, overwrites files

4
"No documentation trail"

Can't track what was built or why

5
"Small change breaks everything"

Touching one piece destroys the whole flow

6
"'Working' solution with mocked data"

Fake users, dummy endpoints, no real testing

7
"Burns through your Cursor usage quota"

Wasteful token usage from constant re-explaining

Burning tokens on trial-and-error Hours wasted

Smart AI Planning

Context Engineering
1
"I want user auth"

🎯 Analyzes your setup, creates structured plan

2
"Breaks into clear phases"

Database → Routes → Middleware → Frontend → Tests

3
"Each task references previous"

Perfect continuity, no conflicts or overwrites

4
"Complete documentation trail"

PRD → Blueprint → Tasks with full context

5
"Changes are surgical"

Modify one piece, everything else stays intact

6
"Production-ready implementation"

Real data, proper validation, comprehensive tests

7
"Maximizes your existing Cursor subscription"

By reducing token waste and repeated explanations

Optimized token usage First-time accuracy

How Context Engineering Works

Add our Context Engineer MCP in Cursor and ask to plan your next feature. As simple as that.

Codebase Analysis

🏗️ Foundation - Your architecture, patterns, and conventions

Context flows down

Smart PRD

📋 Codebase insights + Your requirements

Context grows richer

Technical Blueprint

🏗️ Codebase + PRD = Implementation plan

Perfect context achieved

Actionable Tasks

📋 Complete context = Ready to code

See The Context Engineer in Action

Context Engineer Demo

Watch Demo on YouTube

Video embedding not available - click to view directly

Get early access to lock in the launch price!

Your AI gets a complete project briefing every time

Like having a Senior PM and Senior Architect and Tech Lead always with you.

Generated by your Senior Product Manager

Product Requirements Document: TaskFlow Smart Assignment System

1. Overview & Vision

The Smart Task Assignment System will enhance TaskFlow's productivity dashboard by automatically suggesting optimal task assignments based on team member skills, current workload, and historical performance data. This AI-powered feature aims to reduce manual assignment overhead by 60% while improving task completion rates.

2. Problem Statement

Development team leads currently spend 2-3 hours weekly manually assigning tasks, often leading to suboptimal decisions. This results in:

  • • Uneven workload distribution across team members
  • • Tasks assigned to developers without relevant skill sets
  • • Delayed project timelines due to assignment bottlenecks
  • • Reduced team satisfaction from mismatched task complexity

3. Target Users

Primary Persona: Development Team Leads

  • Demographics: Senior developers, tech leads, and engineering managers
  • Behavior: Manage 5-12 person development teams with varied skill levels
  • Pain Points: Time-consuming manual assignment, difficulty tracking team capacity
  • Goals: Efficient task distribution, improved team productivity, better project outcomes

4. Success Metrics

  • Assignment Accuracy: 85%+ team lead approval rate for AI suggestions
  • Time Savings: 60% reduction in manual assignment time per sprint
  • Team Satisfaction: 4.2+ rating for task-skill matching (5-point scale)

Note: This is a condensed preview. The full PRD contains detailed user stories, acceptance criteria, technical considerations, and implementation phases.

Generated by your Senior Software Architect

Technical Implementation Blueprint: Smart Assignment System

1. Current vs Target Analysis

1.1 Current System Architecture

graph TD Client[React Frontend
TaskFlow Dashboard] --> API[Node.js API Server
:3001] API --> Auth[Authentication
JWT Middleware] API --> Tasks[Task Controller
src/controllers/tasks.js] API --> Users[User Controller
src/controllers/users.js] Tasks --> TaskService[Task Service
Manual Assignment Logic] Users --> UserService[User Service
Profile Management] TaskService --> DB[(PostgreSQL Database
Tasks Table
Users Table)] UserService --> DB API --> Config[Config
src/config/database.js] Config --> Env[Environment Variables
DB_HOST
JWT_SECRET]

1.2 Target System Architecture

graph TD Client[React Frontend
TaskFlow Dashboard] --> API[Node.js API Server
:3001] API --> Auth[Authentication
JWT Middleware] API --> Tasks[Enhanced Task Controller
src/controllers/tasks.js] API --> Assignment[Assignment Controller
src/controllers/assignment.js] Assignment --> MLService[ML Assignment Service
src/services/ml_assignment.js] MLService --> SkillAnalyzer[Skill Analyzer
Team Capability Assessment] MLService --> WorkloadBalancer[Workload Balancer
Capacity Distribution] MLService --> HistoryAnalyzer[History Analyzer
Performance Patterns] Tasks --> TaskService[Task Service
Enhanced with AI Suggestions] TaskService --> DB[(PostgreSQL Database
Tasks + Skills + Assignments)] MLService --> Redis[(Redis Cache
ML Model Results
Assignment Scores)]

1.3 Current Data & Logic Flow

sequenceDiagram participant User as Team Lead participant Frontend as React Dashboard participant API as Node.js API participant TaskService as Task Service participant DB as PostgreSQL User->>Frontend: Create new task Frontend->>API: POST /api/tasks API->>TaskService: Process task creation TaskService->>DB: Store task data DB-->>TaskService: Confirm storage TaskService-->>API: Return task ID API-->>Frontend: Task created response User->>Frontend: Manually assign to team member Frontend->>API: PUT /api/tasks/:id/assign API->>DB: Update task assignment

1.4 Target Data & Logic Flow

sequenceDiagram participant User as Team Lead participant Frontend as React Dashboard participant API as Node.js API participant Assignment as Assignment Service participant ML as ML Service participant Cache as Redis Cache participant DB as PostgreSQL User->>Frontend: Create new task Frontend->>API: POST /api/tasks API->>Assignment: Request AI assignment suggestion Assignment->>ML: Analyze team capacity & skills ML->>Cache: Check cached analysis Cache-->>ML: Return cached or compute new ML->>DB: Query historical performance DB-->>ML: Return performance data ML-->>Assignment: Return assignment scores Assignment-->>API: Suggest optimal assignee API-->>Frontend: Task + AI suggestion User->>Frontend: Accept or modify suggestion

2. Implementation Phases

Phase 1: Database Schema

  • • Skills tracking tables
  • • Assignment history schema
  • • Performance metrics storage

Phase 2: ML Service

  • • Skill analysis algorithms
  • • Workload balancing logic
  • • Performance prediction models

3. Data Models

user_skills

Column Type Constraint Description
user_id INTEGER FK → users Reference to users table
skill_name VARCHAR(100) NOT NULL Technology or domain skill
proficiency_level INTEGER CHECK (1-5) 1-5 skill rating scale
last_updated TIMESTAMP DEFAULT NOW() When skill was assessed

assignment_history

Column Type Constraint Description
task_id INTEGER FK → tasks Reference to tasks table
assigned_user_id INTEGER FK → users Who was assigned the task
completion_time INTERVAL NULL How long the task took
quality_score DECIMAL(3,2) CHECK (0-5) Performance rating (0.00-5.00)

Architecture Highlight: The ML service operates as an independent microservice, providing assignment suggestions while maintaining existing task management workflows.

Generated by your Senior Tech Lead

Implementation Tasks: Smart Assignment System

Task Breakdown by Phase

✓ 1.0 Database Schema Enhancement

1.1 Create user_skills table with skill tracking schema
1.2 Add assignment_history table for performance tracking
1.3 Create database indexes for optimal query performance
1.4 Write migration scripts for existing tasks table enhancement

✓ 2.0 ML Service Development

2.1 Create src/services/ml_assignment.js with core ML logic
2.2 Implement skill analysis algorithm for team capability assessment
2.3 Build workload balancing engine with capacity distribution

✓ 3.0 API Controller Enhancement

3.1 Create src/controllers/assignment.js for AI suggestions
3.2 Enhance existing task controller with ML integration
3.3 Add API endpoints for assignment suggestions and feedback

✓ 4.0 Frontend Integration

4.1 Update React components to display AI assignment suggestions
4.2 Implement suggestion acceptance/rejection UI with feedback ✅ USER TESTING COMPLETE

○ 5.0 Performance Optimization

5.1 Implement Redis caching for ML model results
5.2 Add background job processing for heavy ML computations

Progress: 78% complete - Core ML service and API integration implemented. Performance optimization and deployment phases remaining.

Get Better Results with Smarter Token Usage

Optimize your existing Cursor usage by giving AI the perfect context every time. Reduce wasted tokens, eliminate back-and-forth clarifications, and get precise results faster.

Stop burning tokens on trial-and-error. Get it right the first time with perfect context.

Uses your existing Claude/GPT
Reduces token waste
Code never leaves your machine

Same AI models, smarter usage, better results

Context Engineering is the new skill in AI

This is the best time in history to build your product, if you have the right tools.

What you get: Simple setup, powerful results

Add one configuration block to your IDE and unlock AI agents that maintain perfect project context.

.cursorrules/mcp_servers.json
Cursor IDE Setup
{
  "mcpServers": {
    "context-engineer": {
      "url": "https://your-server.com/mcp",
      "transport": "sse",
      "env": {
        "ACCESS_KEY": "your-access-key"
      }
    }
  }
}

Simple pricing for indie developers

One plan, all features, no corporate BS. Built by developers, for developers.

🚀 Launch Special - Limited Time

Context Engineer

$9 /month $29
Save $20/month - Early Bird Forever!

Everything included, forever at launch price

Zero External AI Costs - Runs directly in Cursor
Comprehensive Document Creation - Auto-generates PRD, Technical Blueprint & Task Lists
Universal Codebase Analysis
Autonomous Workflow - Complete planning pipeline with minimal human intervention
MCP Protocol Compliant - Native Cursor integration, no external tools needed
Intelligent Feature Categorization - Auto-detects single vs multi-category features
Complex Feature Intent Detection - Recognizes complex asks & triggers structured planning
Dynamic Question Generation - Creates context-aware questions based on your actual code
Enhanced Context Gathering - Deep LLM analysis of your specific architecture & patterns
File-Specific Integration Guidance - References your actual files, functions & patterns
Multi-Category Feature Support - Handles complex features spanning multiple domains
Session Management - Resume workflows across multiple sessions

What You Get

Optimized Cursor Usage

Reduce token waste and get precise results on the first try with perfect project context.

Auto-Generated Documents

PRD, Technical Blueprint, and Task Lists based on your codebase.

Universal Codebase Analysis

Works with any language, framework, or architecture.

Autonomous Workflow

Complete planning pipeline with minimal human intervention.

Technical Advantages

2-Minute Setup

One MCP config line. No API keys, no external accounts.

MCP Protocol Compliant

Native Cursor integration, works with any MCP-compatible IDE.

100% Private

All analysis happens on your machine. Code never leaves your computer.

⚡

Limited Time Launch Special

Lock in $9/month forever. First 500 users only. Price increases to $29/month after launch period.

Price locked forever
All future features included
First 500 users only

Frequently Asked Questions

Setting up Context Engineering in Cursor is straightforward. Add this configuration to your MCP settings: 

{
  "mcpServers": {
    "context-engineer": {
      "url": "https://your-server.com/mcp",
      "transport": "sse",
      "env": {
        "ACCESS_KEY": "your-access-key"
      }
    }
  }
}

❓ Does Context Engineering work with VS Code, Claude, and other IDEs?

Yes. If the IDE supports MCP (Model Context Protocol), it works. Here's how to install your Context-Engineer MCP server across platforms — all confirmed and up-to-date as of July 2025.

🧠 Cursor (Native MCP)

Add this to your settings.json:

{
  "mcpServers": {
    "context-engineer": {
      "url": "https://your-server.com/mcp",
      "transport": "sse",
      "env": { "ACCESS_KEY": "your-access-key" }
    }
  }
}

➡️ Tools will appear automatically in the Cursor sidebar.

🧩 VS Code (Agent Mode)

Requires: Agent Mode (Insiders or later builds)

Option A: Command Palette Setup
  1. Open Command Palette
  2. Run: Agent: Add MCP Server
  3. Enter:
    • Name: context-engineer
    • URL: https://your-server.com/mcp
    • Transport: sse
    • Env: Add your access key
Option B: Manual JSON Setup

Add this to .vscode/settings.json:

{
  "agent.mcp.servers": {
    "context-engineer": {
      "url": "https://your-server.com/mcp",
      "transport": "sse",
      "env": { "ACCESS_KEY": "your-access-key" }
    }
  }
}

🌊 Windsurf

Windsurf has a GUI-based setup:

  1. Open Windsurf
  2. Go to: Settings → Cascade → MCP Servers
  3. Click Add Custom Server
  4. Paste the same JSON as above
  5. Click Install, then Refresh Tools

➡️ Your tools will show up in the command bar.

🤖 Claude Code (CLI)

Run this in your terminal:

claude mcp add --transport sse context-engineer https://your-server.com/mcp --header "Authorization: Bearer your-access-key"

➡️ Restart Claude CLI — tools load automatically.

💻 Claude Desktop (GUI)

As of June 2025, Claude Desktop supports MCP via GUI:

  1. Open Settings → Extensions → Add Server
  2. Fill in:
    • Name: context-engineer
    • URL: https://your-server.com/mcp
    • Transport: sse
    • Header or Env: ACCESS_KEY or Authorization
  3. Restart the app

➡️ Tools will appear in the Claude UI.

✅ Summary Table

Platform MCP Support Setup Method Ease
Cursor ✅ JSON in settings ⭐ Easiest
VS Code ✅ UI or JSON Very smooth
Windsurf ✅ GUI + pasted JSON Solid
Claude Code (CLI) ✅ CLI command Fast
Claude Desktop ✅ GUI setup One-click

🛠 TL;DR

  • Works with Cursor, VS Code, Windsurf, Claude CLI, and Claude Desktop
  • Installation is simple: via JSON, GUI, or CLI
  • Once set up, tools appear automatically and work with full project context

Need help? DM me on X @alessiocarra_

Your code never leaves your machine. All LLM operations happen 100% locally using your IDE's existing connection.

Context Engineering works through the MCP protocol directly in your IDE. Everything stays on your computer, just like your regular coding workflow.

No catch! As an indie developer, I believe in fair pricing. The $9/month launch special is:

  • 💡 Locked forever - your price will never increase
  • 🚀 All features included - no premium tiers or paywalls
  • 🎯 Early adopter reward - first 500 users get this price

Requirements:

  • Node.js 16+
  • MCP-enabled IDE
  • Internet connection
  • Windows/Mac/Linux

Works with:

  • JavaScript/TypeScript
  • Python
  • React/Next.js
  • Most web frameworks

Installation takes under 2 minutes. Works locally with your IDE's internet connection (same as Cursor needs).

You can reach me on X directly. I'll respond to all messages as soon as possible: Alex (@alessiocarra_)

Manual prompting requires you to explain your project architecture every single conversation. Context Engineering automates this completely.

❌ Manual Prompting:

  • • Re-explain project structure every time
  • • Copy-paste file contents manually
  • • AI forgets context after 3 messages
  • • Inconsistent with your patterns
  • • Hours wasted on setup explanations
  • • Wastes tokens on repeated explanations
  • • Burns through usage with trial-and-error

✅ Context Engineering:

  • • Automatically analyzes your codebase
  • • Understands your architecture patterns
  • • Perfect context every conversation
  • • Respects your existing code style
  • • Zero setup time per feature
  • • Reduces token waste significantly
  • • Gets accurate results on first try
  • • Maximizes your existing Cursor subscription

It's like having a full senior team of PMs, Architects, and Engineers who actually reads your entire codebase before giving advice.

You're right — this is an early-stage product. But here's why I'm confident it works:

  • 🛠️ I use it every day: This is the tool I built for myself and use daily. If it breaks, my own development stops.
  • 📞 Direct access to me: Issues? Questions? Hit me up on X (@alessiocarra_) — I respond personally.
  • 🚀 Early adopter benefits: Your feedback directly shapes the product. You're getting in on the ground floor of something that works.

Hey, I'm Alex

Alex Carra, Founder of Context Engineering

When I started building with AI, I was excited. Finally — a coding partner that never gets tired and helps me move faster. At the beginning, it felt great. Things were working.

But as the project got bigger, the problems started. The repo turned into a mess. Files everywhere, duplicated logic, random folders. The AI kept making changes that looked smart but caused issues later.

One time I was impressed that a new feature worked — until I realized the AI had faked everything with dummy data, just to make it look like it worked. Another time it removed important parts of a test just to make it pass.

I wasn't coding with AI anymore. I was babysitting it.

After years of building products and writing code, I knew this couldn't scale. So I started experimenting — slowly, manually, one piece at a time — testing different ways to give AI the right context until it finally started working the way I needed. It wasn't fancy, but it delivered real results. No expensive tools, no magic. Just a better way to work with the AI you already use in Cursor.

I built it for myself first. That's how Context Engineering was born. And yes — I used it (in its roughest form) to build itself.

If you're working with AI and tired of fixing its mess, you probably need this too.

Questions? Hit me up on X Alex — I respond to everything.