Claude Context Window Problems: Why MCP Tools Eat 70% of Your Context
If you're building AI agents with Claude and MCP tools, you've probably hit this frustrating wall: your context window fills up after quickly messages, auto-compact kicks in constantly, and your agent forgets what it was doing.
You're not alone. This is the MCP context window problem, and it's breaking production AI workflows for thousands of developers.
The Problem: MCP Tool Descriptions Consume Your Context Window
Here's what happens when you use Claude with MCP tools:
1. MCP tool definitions are sent with every request
Even if you don't use them, Claude receives the full description, parameters, and prompts for every configured MCP tool. With 5-10 MCP servers active, that's easily 30K-50K tokens before you even start your conversation.
2. Tool responses are massive
A single file read returns 50K+ tokens. A codebase search? 80K tokens. An API call? 30K tokens. After 3-4 tool calls, your 200K context window is full.
3. Auto-compact kicks in
Claude automatically summarizes your conversation to save space. The summary itself consumes 90%+ of your remaining context window.
4. Your agent forgets everything
Critical conversation history is lost. The agent loses track of its task. Your workflow breaks.
Real Example from Production
One developer at Autodesk reported having 50 MCP tools configured. They were consuming 60-70% of their context window before sending a single message. After 2 messages with tool calls, auto-compact would trigger. They had to manually add/remove MCP servers before each session just to function.
Simple Solutions to Get By
Solution 1: Manually Manage MCP Servers
What it is: Only enable MCP servers you need for each specific task. Add/remove servers manually.
Why it doesn't scale: You're spending more time managing MCP servers than building features. Doesn't work for production agents that need access to all tools dynamically.
Solution 2: Write Custom MCP Servers with Limited Responses
What it is: Write custom MCP servers that deliberately limit response sizes. Truncate file contents, limit search results, etc.
Why it doesn't scale: You're building workaround logic into every tool. If you truncate too much, the agent doesn't have enough information. If you don't truncate enough, context still fills up.
Solution 3: Implement Progressive Search Manually
What it is: Teach your agent to search iteratively: broad query → narrow results → read specific items. Requires custom prompt engineering and workflow design.
Why it doesn't scale: You're implementing complex search logic that should be framework-level. Brittle, requires constant tuning, and still fills context after 5-10 operations.
The Real Solution: Code Mode (Execution Outside Context Window)
Code Mode changes the fundamental architecture: tool execution happens outside the LLM's context window.
Instead of calling tools directly and returning massive responses, Code Mode presents MCP servers as code APIs. The agent writes code that calls these APIs, the code executes in a sandbox outside the context window, and only concise summaries return to the agent.
How It Works
- 1. Agent writes code - Instead of calling tools, the agent generates code that uses MCP servers as APIs.
- 2. Code executes in isolated sandbox - The code runs outside Claude's context window in a secure sandbox environment.
- 3. Only summaries return to agent - Instead of 50K token responses, Claude gets a concise summary: "Found 3 files matching your query."
- 4. Progressive search built-in - The agent naturally refines queries in code: search → filter → read specific files. No manual implementation needed.
- 5. Parallel execution - Multiple tool calls run simultaneously. Results cached outside context.
Code Mode Platforms: Anthropic vs Cloudflare vs Port of Context
Three platforms offer Code Mode execution: Anthropic's Advanced Tool (Claude only), Cloudflare's Agents SDK (limited models), and Port of Context (model-agnostic, open source).
Anthropic Code Mode (Advanced Tool)
What it is: Anthropic's proprietary Code Mode implementation available through their Claude API.
Limitations: Claude only. Proprietary service. Limited customization. Vendor lock-in.
Cloudflare Agents SDK
What it is: Cloudflare's edge-based Code Mode platform with Dynamic Worker Loader API.
Limitations: Cloudflare network only. Limited to their model selection. Workers runtime constraints. Vendor lock-in.
Port of Context (pctx)
What it is: Open-source Code Mode platform that works with any LLM. MIT licensed, self-hosted, model-agnostic.
Advantages: Use any LLM (Claude, GPT, Gemini, local models). Deploy anywhere (AWS, GCP, your laptop). Full control. No vendor lock-in. TypeScript compiler included.
Real Results: What Changes with Code Mode
Unlimited Tool Calls
Go from 3-4 tool calls before hitting context limits to 100+ operations in a single workflow. Your agent can search, filter, read, analyze, and write without running out of space.
No More Auto-Compact
Tool execution happens outside the context window, so auto-compact never triggers. Your agent maintains full conversation history throughout the entire workflow.
Production-Ready Workflows
Build agents that can handle complex, multi-step tasks without manual MCP server management or custom response limiting logic.
Ready to Solve Your Claude Context Window Problems?
Port of Context (pctx) is an open-source Code Mode platform that executes tools outside the context window with no vendor lock-in and support for any LLM.