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contextburn

MCP

MCP server reporting token-efficiency metrics for coding agents — the share of paid tokens that became output versus context re-reads.

by arsentev-ai·arsentev-ai/contextburn·Python·v0.2.1
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uvx contextburn
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About

contextburn reads the transcripts Claude Code already writes on your machine and tells you what share of the tokens you paid for became model output — and how much was the agent re-reading context it had already sent.

Token counters answer "how much did I spend?". This answers "how much of it was work?" — a normalised share, so it can be compared across sessions, models and ways of working.

Read more on GitHub →
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token-efficiencymonitoring#token-efficiency#metrics#coding-agents#context-window#cost-efficiency#llm#observability#token-usage