context-engineering

This MCP context is based on the Context Engineering repository, which serves as a companion resource for exploring how to design and reason about context for LLM-driven systems. The project focuses on concepts, patterns, and examples around system instructions, external knowledge, memory, tools, and state management that support robust AI agent behaviors. While there isn’t a dedicated, runnable MCP server described in the README, you can treat this as a reference implementation: the context engineering examples illustrate how to structure context and provide reusable prompts, prompts templates, and workflow patterns that you can adapt to your own MCP-enabled agent infrastructure. Use the included materials to model context inputs, retrieval strategies, and tool usage in your own MCP-enabled setup.

Prerequisites:

• Node.js (recommended if you plan to run Node-based examples in this repository)
• Git

Step-by-step:

1. Clone the repository git clone https://github.com/bonigarcia-context-engineering.git cd bonigarcia-context-engineering

2. Install dependencies (if a Node.js server/example is provided) npm install

3. Run the local MCP-enabled example (adjust the command to match your setup) node server.js

4. (Optional) Set up environment variables for your environment as needed, e.g. API keys for memory/storage backends or retrieval plugins.

Note: The README describes a companion resource and examples rather than a single published MCP server. Adapt the patterns and examples to your own MCP runner or agent framework as appropriate.

Tips and caveats:

• This repository positions context engineering as a broader discipline; specific MCP server configurations are not provided, so you may need to adapt the concepts to your own MCP runner.
• If you implement your own MCP server, consider exposing environment-based configuration for memory, retrieval, and tool access to simplify deployment.
• When integrating with LLMs, ensure context components (instructions, history, tools, external data) are clearly segmented and revocable to avoid leaking sensitive information.
• Common issues include mismatched memory backends, missing tool adapters, and retrieval errors. Start with a minimal viable context (instructions + a small tool set) and iterate.
• If you publish an MCP-enabled service based on this work, document the exact context components and adapters your server uses to help users reproduce and extend your setup.