Model-Context Protocol (MCP) is a standardized layer that allows an LLM to communicate with various software tools without needing custom integrations for each. It acts like a universal translator, enabling the LLM to 'speak English' while the MCP handles communication with each tool's unique API.

The vision for Model Context Protocol (MCP) is to let AIs perform complex, multi-app tasks. However, translating a full API like Stripe's into MCP tools overwhelms current models' context windows, making them confused and ineffective. This forces developers to handcraft a small subset of tools.

The shift from 'prompt engineering' to 'context engineering' reframes AI interaction. Instead of just conversing with an AI, you are designing the entire information ecosystem—including specs, visuals, and data—that the model needs to perform its task effectively.

Instead of one large context file, create a library of small, specific files (e.g., for different products or writing styles). An index file then guides the LLM to load only the relevant documents for a given task, improving accuracy, reducing noise, and allowing for 'lazy' prompting.

The MCP protocol's primitives are not directly influenced by current model limitations. Instead, it was designed with the expectation that models would improve exponentially. For example, "progressive discovery" was built-in, anticipating that models could be trained to fetch context on-demand, solving future context bloat problems.

The early focus on crafting the perfect prompt is obsolete. Sophisticated AI interaction is now about 'context engineering': architecting the entire environment by providing models with the right tools, data, and retrieval mechanisms to guide their reasoning process effectively.

Developing LLM applications requires solving for three infinite variables: how information is represented, which tools the model can access, and the prompt itself. This makes the process less like engineering and more like an art, where intuition guides you to a local maxima rather than a single optimal solution.

Exposing a full API via the Model Context Protocol (MCP) overwhelms an LLM's context window and reasoning. This forces developers to abandon exposing their entire service and instead manually craft a few highly specific tools, limiting the AI's capabilities and defeating the "do anything" vision of agents.

Top-tier language models are becoming commoditized in their excellence. The real differentiator in agent performance is now the 'harness'—the specific context, tools, and skills you provide. A minimalist, well-crafted harness on a good model will outperform a bloated setup on a great one.