Local Code Indexing for Cursor: Blazing-Fast Private Mastery

What is Local Code Indexing for Cursor: Blazing-Fast Private Mastery?

This experimental solution leverages a Python-based server to create private, local indexes of your codebases using ChromaDB. By exposing a semantic search interface via Model Context Protocol (MCP), it empowers tools like Cursor with instant access to your code's semantic context—all while keeping your intellectual property securely on-premises.

How to Use Local Code Indexing for Cursor: Blazing-Fast Private Mastery?

1. Clone the repository and configure project paths in .env
2. Deploy via Docker Compose to initialize the indexing server
3. Configure Cursor's MCP settings to point to http://localhost:8978/sse
4. Enforce semantic search in your .cursorrules with priority over traditional grep methods
5. Activate Cursor's Agent mode to see real-time vector searches in action

Key Features of Local Code Indexing for Cursor: Blazing-Fast Private Mastery?

• Zero-cloud dependency: Full control over sensitive code assets
• Blazing speed: ChromaDB's optimized vector storage cuts search latencies
• Deep IDE integration: MCP protocol enables context-aware suggestions within Cursor
• Configurable scope: Selective indexing of critical projects via comma-separated folders

Use Cases of Local Code Indexing for Cursor: Blazing-Fast Private Mastery?

Perfect for:

• Instant codebase navigation during pair programming sessions
• Security audits requiring fast pattern recognition across repositories
• Onboarding developers with semantic search-driven knowledge discovery
• Debugging workflows needing context-aware stack trace analysis

FAQ from Local Code Indexing for Cursor: Blazing-Fast Private Mastery?

Does this require Docker expertise?

No—docker-compose handles all orchestration out-of-the-box

How often does indexing update?

Index rebuilds occur automatically when project files are modified

Can I use non-Python projects?

Yes—the indexer processes all text-based code files regardless of language

What's the minimum hardware spec?

8GB RAM recommended for projects over 50k files; SSD storage strongly advised

Local Code Indexing for Cursor

An experimental Python-based server that locally indexes codebases using ChromaDB and provides a semantic search tool via an MCP (Model Context Protocol) server for tools like Cursor.

Setup

1. Clone and enter the repository:

   git clone

cd cursor-local-indexing

2. Create a .env file by copying .env.example:

   cp .env.example .env

3. Configure your .env file:

   PROJECTS_ROOT=~/your/projects/root # Path to your projects directory

FOLDERS_TO_INDEX=project1,project2    # Comma-separated list of folders to index

Example:

    PROJECTS_ROOT=~/projects
FOLDERS_TO_INDEX=project1,project2

4. Start the indexing server:

   docker-compose up -d

5. Configure Cursor to use the local search server: Create or edit ~/.cursor/mcp.json:

   {
   "mcpServers": {
   "workspace-code-search": {
   "url": "http://localhost:8978/sse"
   }
   }

}

6. Restart Cursor IDE to apply the changes.

The server will start indexing your specified projects, and you'll be able to use semantic code search within Cursor when those projects are active.

7. Open a project that you configured as indexed.

Create a .cursorrules file and add the following:

<instructions>
For any request, use the @search_code tool to check what the code does.
Prefer that first before resorting to command line grepping etc.
</instructions>

8. Start using the Cursor Agent mode and see it doing local vector searches!