Stop Burning Your Context Window – How We Cut MCP Output by 98% in Claude Code
A new tool called 'Context Mode' dramatically slashes the context window usage for AI agents interacting with external tools, achieving up to a 98% reduction in token consumption. By acting as an intermediary, it intelligently prunes verbose raw data from tool outputs, allowing Claude Code sessions to run significantly longer and more efficiently. This technical optimization addresses a critical bottleneck in large language model applications, garnering strong interest from developers building multi-tool AI workflows.
The Lowdown
The 'Context Mode' project introduces an innovative solution to a pervasive problem in AI agent development: the rapid consumption of an LLM's context window by unoptimized tool outputs. By interposing itself between Claude Code and external tool calls, Context Mode filters and condenses raw data, enabling agents to operate more effectively and for extended durations.
- The Problem: Standard Multi-tool Cooperative Protocol (MCP) tools dump verbose raw data (e.g., Playwright snapshots, GitHub issue lists, access logs) directly into the LLM's context window, quickly consuming tokens and shortening usable session times.
- The Solution - Context Mode: This server-side component sits between Claude Code and tool outputs, drastically reducing the data volume before it enters the LLM's context.
- Technical Implementation: It operates by spawning isolated subprocesses for tool execution, capturing only the relevant
stdout. Raw data never leaves the sandbox. For knowledge retrieval, it leverages SQLite FTS5 with BM25 ranking and Porter stemming to return precise, indexed content. - Dramatic Efficiency Gains: Real-world scenarios demonstrate reductions of up to 98% (e.g., a 56 KB Playwright snapshot becomes 299 B, 20 GitHub issues from 59 KB to 1.1 KB). This extends effective session time from minutes to hours.
- Seamless Integration: Context Mode can be installed via the Claude Code Plugin Marketplace or directly as an MCP tool, automatically routing tool outputs for optimization.
- Motivation: The author, observing a common pattern of raw data dumping across many MCP tools, built Context Mode to mirror Cloudflare's input compression efforts by focusing on output optimization.
In essence, Context Mode acts as a highly effective data bouncer, ensuring that only the essential information from tool interactions ever reaches the LLM, thereby unlocking significantly longer and more productive AI agent development sessions.
The Gossip
Contextual Compression Commendations
Many commenters expressed enthusiasm and appreciation for the project, immediately recognizing the value of its context reduction capabilities. Users noted the critical nature of solving the 'context bloat' problem, especially in multi-step workflows. Some shared their own positive experiences, highlighting significant token usage reductions, while others drew parallels to early web development, suggesting a new era of optimization for 'coding agents.'
Querying Core Concepts
Users posed insightful questions regarding the tool's underlying mechanisms and scope. Key inquiries included how credential passthrough functions across isolated subprocesses, how Context Mode interacts with the broader MCP context, and whether its 'pre-compaction' approach might unintentionally omit relevant data in edge cases. The author (mksglu) actively engaged, clarifying that credential passthrough uses an explicit allowlist of environment variables and that the tool primarily addresses output data, not the MCP context itself.
Future Frontiers of Flow Management
The discussion branched into broader ideas for advanced context management beyond just output compression. Suggestions included 'backtracking' to prune failed attempts from context, enabling more fine-grained control over context manipulation (rather than a simple stack), and empowering agents to manage their own context dynamically. These ideas point towards a desire for more sophisticated 'memory management' within AI agent workflows.