Context Engineering - How to give your AI agents the Perfect Context for every task

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:brain: Build Smarter with AI Agents That Stay On Track — Introducing Context Engineer MCP

If you’ve ever tried building non-trivial features with an AI agent and felt like it kept forgetting what it was doing halfway through… you’re not alone.

I built Context Engineer, an MCP server for Cursor, to fix that.

What It Does

Context Engineer gives your AI agents structured context before coding starts, so they know exactly what to do — and how.

It generates:

  • A PRD (Product Requirements Doc): written in plain English, grounded in your actual codebase.
  • A Technical Blueprint: architectural plan based on your existing tech stack and code patterns.
  • An Actionable Task List: broken down step-by-step like a senior dev would do.

Instead of writing code from vague prompts, the AI follows a full spec with context baked in.

Why It Matters

When agents lose track, you waste time, tokens, and trust.

Context Engineer ensures:

  • :white_check_mark: The AI understands your architecture before writing code
  • :white_check_mark: It follows clear tasks, not fuzzy guesses
  • :white_check_mark: Output fits your codebase and style — no hallucinated APIs or rewrites

It’s like giving your agent a PM, architect, and tech lead to guide the task.

How To Use It in Cursor

Just add this to your .cursor/config.json:

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

No changes to your codebase. No extra complexity. Just clean structure, fed into your existing AI workflows.

I’d love your thoughts and feedback — and to see what you build with it.

You can access it here: https://contextengineering.ai/

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