Cursor Token Usage Too High?
Fix It in 30 Seconds
If you're seeing "rate limit exceeded", slow responses, or a shocking API bill from Cursor, the problem is simple: Cursor sends way too much context to the LLM on every request.
On a 50K+ line codebase, each Cursor request can send 80,000–200,000 tokens. That's $0.24–$0.60 per question at Claude Sonnet pricing.
Entroly compresses that context 70–95% — same accuracy, fraction of the tokens.
The Fix: 3 Steps
pip install entroly
cd /your/project && entroly go
Restart Cursor. Done. Entroly runs as an MCP server — Cursor uses it automatically.
Why Cursor Uses So Many Tokens
When you ask Cursor a question, it builds a context prompt from:
- Open files in your editor (full contents)
- Recently edited files
- @-mentioned files and symbols
- Terminal output
- Codebase index results
On medium-to-large codebases, this routinely exceeds 100K tokens. The LLM only needs a fraction of this to answer your question correctly.
What Entroly Does
Entroly uses information theory (Shannon entropy, 0/1 Knapsack optimization, BM25 ranking) to select the optimal subset of code that answers your specific question. It sends only what matters, keeping everything else available via Content-Compressed Retrieval handles that the AI can fetch if needed.
This approach is fundamentally different from truncation (which loses information) or embedding-based RAG (which requires GPU and misses structure). Entroly is lossless — every omitted fragment is recoverable.
Also Works With
- Claude Code — same MCP integration, same compression
- Windsurf — auto-configured via
entroly go - Cline — endpoint-based integration
- Codex CLI, Aider, Gemini CLI — env-wrap integration
- Any OpenAI/Anthropic API client — via the local proxy on
localhost:9377