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Context Engineer MCP – Fixing Context Loss in AI Coding Agents (contextengineering.ai)
1 point by bralca88 2 days ago | hide | past | favorite | 1 comment




When I started building features with AI coding agents (Cursor, Claude Code, etc.), I noticed the main failure was not the model itself, it was context. • Too little context → hallucinations. • Dumping the whole repo → confusion and inconsistent outputs. • Across prompts, the agent forgot my architecture and conventions.

At first, I worked around it manually: • Writing PRDs and tech specs that compared current vs. target state. • Creating step-by-step task lists so the agent worked incrementally. • Copy-pasting relevant files into prompts to keep outputs grounded.

It worked, but it did not scale.

So I wrapped the process into Context Engineer MCP, a server that: • Analyzes your repo and tech stack. • Captures coding styles, naming patterns, and conventions. • Generates PRDs, technical blueprints, and task lists before coding starts. • Runs locally, so no code ever leaves your machine.

The surprising part: with context handled, I shipped production features entirely vibe-coded without the usual drop in quality as the codebase grew.

I would love feedback from HN, both from people experimenting with AI-assisted coding and from anyone thinking about better ways to manage context for dev tools.




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