MCP and Context Engineering: The Protocol Reshaping How Agents Read the World

Two years ago, connecting a language model to an external tool meant writing custom glue code, maintaining it, and hoping the API wouldn't change versions. Today, according to an analysis published this week by InfoWorld, the Model Context Protocol has reconfigured that equation by positioning context management as its own discipline, called context engineering, rather than a plumbing problem each team solved independently.

The article arrives at a moment when MCP has already been established for several months as the de facto standard for integrations with Claude and increasingly with other LLMs. It's not news that MCP exists; what is relevant is the underlying argument that InfoWorld develops, and it deserves attention.

What is context engineering and why MCP fits there

Context engineering starts from a simple observation: a model's performance depends not only on its parameters, but on what information reaches the model, when, and in what format. A Claude Opus 4.7 with a one-million-token window has plenty of raw capacity; the real problem is deciding what to put in that window so the response is useful.

MCP addresses this with a client-server architecture in which MCP servers expose tools, resources, and prompts under a standardised interface. The model doesn't need to know whether it's pulling from a vector database, a REST API, or a local filesystem: it receives structured context through the same contract. This converts the protocol into an abstraction layer for context, not just for function calls.

The distinction matters. Until now, many teams thought of MCP primarily as a sophisticated tool calling mechanism. InfoWorld's approach, and the one we're seeing grow in the integrator community, is different: MCP as infrastructure for deciding what the agent knows at each moment of its lifecycle.

Where this shows up in practice

In the Claude ecosystem, this translates into several pieces that already work together:

• MCP servers configured in `claude_desktop_config.json` or from Claude Code allow different agents to share the same context server without duplicating logic.
• Claude Code hooks (PreToolUse, PostToolUse, Stop) allow you to intercept the lifecycle to enrich or filter context before it reaches the model, closing the loop between context engineering and flow control.
• Skills and subagents can consume context served by specialised MCP servers, which avoids putting in the main prompt information that only a specific subagent needs.

The practical result is that the best-designed systems today don't put everything in context from the start: they inject it on demand, based on the agent's state and the current task. MCP is the mechanism that makes this possible in a reproducible way.

For whom this matters

The InfoWorld piece is aimed at technical profiles with prior knowledge of LLMs, but the implications are broad:

• Teams maintaining Claude integrations will see in this approach a guide for refactoring architectures where context grows uncontrollably.
• Platform engineers evaluating how to standardise access to internal tools will find in MCP a solid argument for not reinventing the contract each time.
• Plugin and MCP server developers have here a validation that work at the context layer has more long-term impact than optimising the prompt itself.

What the article doesn't address, and it's a gap, is the observability side: knowing what context a model received in a specific call remains difficult, and MCP doesn't solve that on its own. It's the next problem the community will need to work on.

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From our perspective, the approach seems sound and well-grounded: treating context as a resource that is managed, not as text that is concatenated, is exactly the mindset shift that separates agents that work in production from those that only work in demos. MCP is today the most mature tool for doing this with Claude, although there is still work to be done in the monitoring and debugging layers.