Context Window Management

Keep AI context focused: what to include, what to drop, and when to compact.

Agent DevelopmentIntermediate

Why

MCP's reference server repo has ~90,000 GitHub stars, and the Agentic AI Foundation grew from 49 founding members (December 2025) to 247 by August 2026, signaling rapid multi-agent ecosystem growth. Every model has a finite context window.

Cramming irrelevant files, stale errors, and chat history dilutes attention on the real task. Managing context is the single biggest lever for output quality on long sessions.

When

Actively manage context on sessions >30 messages, large repos, or when output quality degrades mid-session. Avoid multi-agent orchestration for single-step tasks — a single agent call is simpler, cheaper, and more reliable.

How

1. Include only files the answer depends on; reference others by name. 2. Drop stale error logs once fixed; don't let them linger in history. 3. Use /compact (Claude Code) or a new session when context grows. 4. Scope with @file/@folder (Cursor) or --scope (Claude Code). 5. Summarize long outputs before feeding them back.

Example

Before: 60-message chat with 8 full files + 5 stale stack traces → degraded answers. After: /compact, then "@file src/api/router.ts only; here is the current error: <paste>". Answer quality returns because attention focuses on the live problem.

Common Problems

⚠Error: Agent loops infinitely on the same tool call → add a max-iterations guard and a distinctness check on the last N actions.
⚠Error: Cannot find module '@modelcontextprotocol/sdk' → `npm install @modelcontextprotocol/sdk`; agents need the SDK to call MCP tools.
⚠Error: "Tool timed out after 30000ms" → increase the timeout config or break the task into smaller sub-tasks the agent can complete incrementally.

Best Practices

✓Treat context as a scarce resource; include the minimum that resolves the task.
✓Compact proactively at ~50 messages, not after quality drops.
✓Measure the outcome with a concrete metric (time saved, defect rate, test coverage) so the practice can be justified and improved.

FAQ

What is Context Window Management?

Keep AI context focused: what to include, what to drop, and when to compact. It helps developers quickly grasp the core value and use cases of Context Window Management.

How do I get started with Context Window Management?

To get started with Context Window Management, focus on these points first: Include only files the answer depends on; reference others by name.

What are common mistakes with Context Window Management?

Common mistakes when using Context Window Management include: Error: Agent loops infinitely on the same tool call → add a max-iterations guard and a distinctness check on the last N actions.; Error: Cannot find module '@modelcontextprotocol/sdk' → `npm install @modelcontextprotocol/sdk`; agents need the SDK to call MCP tools.; Error: "Tool timed out after 30000ms" → increase the timeout config or break the task into smaller sub-tasks the agent can complete incrementally..

What are the best practices for Context Window Management?

Best practices for Context Window Management include: Treat context as a scarce resource; include the minimum that resolves the task.; Compact proactively at ~50 messages, not after quality drops.; Measure the outcome with a concrete metric (time saved, defect rate, test coverage) so the practice can be justified and improved..

How do I learn more about Context Window Management?

Start with the official docs, then search real-world examples on GitHub. Validate patterns in a small scratch repo before applying them to production code.