How to Engineer Better Context in Claude Code

TL;DR

Improve Claude Code performance by reducing unnecessary context and delegating token-heavy work to subagents. Load MCP servers only when required, keep the always-loaded memory file limited to universal essentials, and use task-specific priming commands for changing needs. Focused context gives the primary agent more room to reason while preserving access to specialized tools, documentation, and workflows.

Transcript

A focused engineer is a performant engineer and a focused agent is a performant agent. Context engineering is the name of the game for high value engineering in the age of agents. So how good are your context engineering skills? Do you have a skill issue? Let's find out and fix it. There are three levels of context engineering and a fourth hidden l... Read More

Key Insights

• Context engineering is the deliberate management of an agent’s limited context window so it remains focused on the current task. The framework presented reduces irrelevant material and delegates token-heavy work, shifting attention from what an engineer can personally do toward what well-prepared agents can accomplish.
• MCP servers are a major source of avoidable startup context when they load automatically without serving the current task. Specialized configuration files let engineers start Claude Code with only the required server, while strict configuration can prevent unrelated global tools from entering the working context.
• An always-loaded memory file is useful only when its contents are universally relevant. Its reusable nature is both its advantage and its weakness because every instruction consumes context during every session, even when the current engineering task has no connection to that information.
• Context priming is a reusable, task-specific prompt that prepares an agent by directing it to read selected resources and report its understanding. It provides control over initial context without forcing every session to inherit a growing collection of static instructions from the project memory file.
• A concise memory file should contain only essentials that every agent must receive in every session. Information relevant to bugs, features, maintenance, documentation, or specialized codebase areas belongs in separate prime commands that engineers can invoke when those particular contexts become useful.
• Subagents are separate workers with partially forked context windows that can keep large intermediate workloads away from the primary agent. Their custom instructions operate as system prompts, while ordinary prompts and reusable commands enter the active conversation as user-provided context.
• Delegating documentation retrieval to subagents prevents web content and scraping output from filling the primary context window. The main agent can coordinate specialized workers, receive their useful results, and preserve more of its own context for decisions, integration, and continued engineering reasoning.
• A focused agent is a more performant agent because irrelevant tools, instructions, and research compete with task-specific information. Effective context engineering therefore requires conscious control of startup state, selective loading of resources, reusable priming workflows, and delegation of expensive supporting work.

Questions & Answers

Q: How can I improve context management in Claude Code?

Improve context management by applying the Reduce and Delegate framework. Reduce startup material by disabling automatically loaded MCP servers, shrinking the project memory file to universal essentials, and invoking task-specific resources only when required. Delegate research, documentation retrieval, and other token-heavy work to subagents so the primary agent retains more context for reasoning, coordination, and implementation.

Q: Why should Claude Code MCP servers be loaded selectively?

MCP servers contribute tool definitions and related material to the context window even when their capabilities are irrelevant to the current task. Loading them selectively prevents unused tools from consuming valuable space. A specialized configuration can start only the server needed for a focused session, while strict configuration can override unrelated global defaults and keep the agent’s initial state controlled.

Q: What should a Claude memory file contain?

A Claude memory file should contain only instructions and information that every agent needs during every session. Because the file loads automatically, project details that matter only for certain bugs, features, chores, or documentation tasks create persistent overhead. Conditional material should move into dedicated priming commands, leaving the memory file concise, stable, and broadly applicable.

Q: What is context priming in Claude Code?

Context priming is the use of a dedicated reusable prompt, implemented as a custom command, to prepare an agent for a specific task type. The command can state its purpose, direct the agent to run checks, identify selected files to read, and request a report. It hotloads relevant knowledge when needed instead of permanently attaching it to every session.

Q: Why is context priming better than a large memory file?

Context priming is more controllable because it loads information according to the current task, while a large memory file enters every session automatically. Engineering needs change across a codebase, but static memory tends to accumulate instructions over time. Separate prime commands provide focused entry points for different activities and avoid burdening unrelated sessions with unnecessary project information.

Q: How should task-specific prime commands be organized?

Task-specific prime commands should correspond to recurring areas of focus within the codebase. A general command can orient an agent by having it inspect the readme and project structure, while separate commands can prepare work on bugs, features, chores, or Claude Code configuration. Each command should use a concise purpose, execution, reading, and reporting structure.

Q: How do subagents preserve the primary context window?

Subagents perform delegated work inside their own partially forked context windows, so their full instructions, retrieved documents, and intermediate processing do not accumulate in the primary agent’s conversation. Custom subagent definitions also act as system prompts rather than ordinary user prompts. The primary agent can receive concise results while retaining space for higher-level reasoning and coordination.

Q: When should Claude Code work be delegated to subagents?

Work should be delegated when it requires substantial reading, web retrieval, documentation processing, or other intermediate context that the primary agent does not need to retain in full. Documentation loading is a clear example because separate workers can fetch and process individual resources. Delegation keeps supporting material isolated while the primary agent manages outcomes and continues the central task.

Summary & Key Takeaways

• Context engineering manages the limited context window through two complementary actions: reducing unnecessary material and delegating work. Common sources of waste include automatically loaded MCP tools and oversized memory files. An effective setup keeps startup context small, introduces specialized resources only when required, and protects the primary agent’s capacity for focused engineering work.

• Task-specific context priming replaces a large static memory file with reusable commands that prepare an agent for a particular kind of work. A prime command can direct the agent to read selected files, understand the relevant codebase structure, and report its findings. Separate commands can support general orientation, bug investigation, features, or maintenance.

• Subagents provide separate context windows for delegated tasks such as retrieving and processing documentation. Their custom definitions function as system prompts without placing the full instructions and working material directly into the primary agent’s context. This keeps token-heavy research isolated while allowing the main agent to coordinate results and continue reasoning with less clutter.