LLM Context Window Budgeter

LLM Context Window Budgeter MCP Connector for Claude

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Monitor and predict LLM context window exhaustion with precision token forecasting.

0 tools Official Updated Oct 1, 2026 Official Vinkius Partner

The LLM Context Window Budgeter is a specialized utility designed to prevent session failures by tracking the remaining input-token budget in Large Language Models. By analyzing your system prompt, conversation history, and reserved output tokens, it provides real-time metrics on context consumption. Use calculate_budget_status to see exactly how much space remains, estimate_remaining_turns to forecast when you will hit the limit based on message size, and analyze_context_risk to receive actionable recommendations like summarization or truncation strategies.

Available Tools

your_tool_name

llmcontext-windowtoken-budgetmonitoringai-agents

0 tools expose this connector's capabilities to your AI agent.

See how to talk to your AI agent using LLM Context Window Budgeter.

I have a 128k context window, 2k system prompt, 50k history, and I want to reserve 4k tokens for the response. What is my current status?

Your remaining input budget is 72,000 tokens, and your context window consumption is approximately 43.75%.

Based on my remaining budget of 20,000 tokens and an average message size of 1,500 tokens, how many more turns can I have?

You can expect approximately 13 full message exchanges before reaching the limit.

My context usage is at 85%. What should I do?

At 85% consumption, the recommended action is to Summarize your conversation history to reclaim space.

The tool subtracts your system prompt tokens, conversation history tokens, and reserved output buffer from your total context window size to determine the remaining input budget. Tools available: `your_tool_name`.

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