How to Give Cursor Your Full Codebase — Fix Context Window Limits

Last updated: April 2026 | Reading time: 6 min

If you've used Cursor on a real project, you've experienced the frustration: Cursor hallucinates function calls, invents APIs that don't exist, and misses obvious dependencies. The root cause isn't Cursor's AI model — it's the context window limit.

The Problem: Cursor Only Sees 5-10 Files

Cursor uses a combination of open tabs, recent files, and embedding-based retrieval to fill the context window. On a typical project with 500+ files, Cursor can only see about 5-10 files per request — roughly 5% of your codebase.

The other 95% is completely invisible to the AI. This means:

• Hallucinated imports — Cursor references modules it can't see
• Invented APIs — it guesses function signatures instead of reading them
• Missed dependencies — changes auth.py without knowing about auth_config.py
• Duplicate code — writes functions that already exist elsewhere
• Incomplete refactors — only updates files it can see, breaking the rest

You've probably worked around this by manually pasting code, writing detailed system prompts, or adding .cursorrules files. There's a better way.

The Fix: Context Engineering with Entroly

Entroly is a context engineering engine that compresses your entire codebase into Cursor's context window at variable resolution:

• Critical files appear in full (the code Cursor needs to edit)
• Supporting files appear as function signatures (types, interfaces, configs)
• Everything else appears as one-line references (so Cursor knows they exist)

The result: Cursor sees all your files, uses 78% fewer tokens, and gives dramatically better answers.

Setup: 2 Minutes with Cursor

Step 1: Install Entroly

pip install entroly[full]

Step 2: Initialize for Cursor

entroly init

This auto-detects Cursor and generates .cursor/mcp.json — the MCP server configuration that connects Entroly to Cursor.

Step 3: Verify

entroly demo

This runs a before/after comparison on YOUR codebase, showing exactly how many tokens you'll save and which files get which resolution level.

Before vs After

How It Works Under the Hood

Entroly runs as an MCP (Model Context Protocol) server that Cursor connects to natively. When you make a request in Cursor:

1. Entroly indexes your codebase — builds a dependency graph, fingerprints every code fragment
2. Scores fragments by information density — high-value code ranks high, boilerplate ranks low
3. Selects the optimal subset — mathematically proven optimal via knapsack solver (not approximate top-K)
4. Delivers at variable resolution — critical files in full, supporting files as signatures, everything else as references
5. Learns from outcomes — reinforcement learning adjusts weights based on which context produced good AI responses

The entire pipeline adds less than 10ms per request. You won't notice it.

Quality Presets

Control the speed vs quality tradeoff:

entroly proxy --quality speed       # minimal optimization, lowest latency
entroly proxy --quality balanced    # recommended for most projects
entroly proxy --quality max         # full pipeline, best results

FAQ

Does it send my code to any external service?

No. Everything runs locally on your machine. Your code never leaves your computer.

Does it work with Cursor's free tier?

Yes. Entroly works with any Cursor plan. In fact, it's even more valuable on the free tier since you have fewer requests — each one needs to count.

Does it replace .cursorrules?

No — they're complementary. .cursorrules gives Cursor instructions about your preferences. Entroly gives Cursor visibility into your actual code. Use both.

What languages does it support?

All of them. Entroly works at the file/fragment level, not the AST level. It supports any text-based source code.

pip install entroly[full] && entroly go