SourcePrep vs. Cursor Indexing
Cursor is an incredible AI code editor, and its built-in codebase indexing is great for finding simple keyword matches. But when you need your AI to understand complex architectures, call chains across microservices, or how a specific interface is implemented across 20 files, basic vector search falls short. SourcePrep isn't a replacement for Cursor—it's a massive upgrade to Cursor's brain via the Model Context Protocol (MCP).
How does SourcePrep improve Cursor?
Cursor relies primarily on BM25/Vector Search to find code chunks that semantically match your prompt. SourcePrep builds a structural trace graph using a local Rust tree-sitter engine. While Cursor grabs isolated chunks of text, SourcePrep understands the syntax: it knows what functions call other functions, where types are defined, and how modules import each other. By connecting SourcePrep to Cursor via an MCP server, Cursor's AI agents can query the structural graph to traverse call chains and gather perfect, highly-compressed context that regular vector search would miss.
Feature Comparison Matrix
| Capability | SourcePrep (via MCP) | Cursor Built-in Index |
|---|---|---|
| Index Type | Structural Graph + Vector | Vector + BM25 Lexical |
| Understands Call Chains | Yes (Tree-sitter AST) | No |
| Multi-Repo Context | Yes (Global Daemon) | Current workspace only |
| Context Compression (LOD) | Yes (Strips function bodies) | No (Raw text chunks) |
| Custom Path Weights | Yes (Boost/deprioritize dirs) | No |
1. The "Lost in the Middle" Problem
When you ask an AI to refactor a complex system, standard codebase indexes grab dozens of file snippets and dump them into the prompt window. Research shows that LLMs suffer from "Context Rot" when flooded with too much raw text—they lose track of the actual instructions.
SourcePrep solves this using Level of Detail (LOD) compression. If SourcePrep determines a file is only peripherally related to your query, it strips out the internal function bodies and only sends the Class names, function signatures, and types to the AI. You get 20x more architectural context using a fraction of the tokens.
2. Multi-Repo Architectures
Cursor is scoped to the workspace you currently have open. If you have a frontend repo, a backend microservices repo, and a shared library repo, Cursor's AI struggles to see the big picture.
The SourcePrep local daemon indexes all of your projects in the background. When Cursor's Agent uses the SourcePrep MCP tool, it can seamlessly pull context from your backend repo while you are actively typing code in your frontend repo.
3. A Match Made in Heaven
You don't have to choose between Cursor and SourcePrep. They are designed to work together. Cursor provides the world-class UX, inline edits, and fast autocomplete. SourcePrep runs silently in the background, providing the structural deep-context that Cursor's Composer agent needs to tackle massive refactors without hallucinating.