Cursor Local Indexing
What is Cursor Local Indexing
Cursor-local-indexing is an experimental server that utilizes ChromaDB to locally index codebases, enabling semantic search capabilities through an MCP server for tools like Cursor.
Use cases
Use cases for cursor-local-indexing include searching through large codebases, improving code navigation in development environments, and enhancing productivity by providing semantic context for code queries.
How to use
To use cursor-local-indexing, clone the repository, set up the environment variables in a .env file, start the indexing server using Docker, and configure Cursor to connect to the local search server. Finally, create a .cursorrules file to guide the search behavior.
Key features
Key features include local code indexing using ChromaDB, semantic search capabilities, easy setup with Docker, and integration with Cursor for enhanced code exploration.
Where to use
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Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Overview
What is Cursor Local Indexing
Cursor-local-indexing is an experimental server that utilizes ChromaDB to locally index codebases, enabling semantic search capabilities through an MCP server for tools like Cursor.
Use cases
Use cases for cursor-local-indexing include searching through large codebases, improving code navigation in development environments, and enhancing productivity by providing semantic context for code queries.
How to use
To use cursor-local-indexing, clone the repository, set up the environment variables in a .env file, start the indexing server using Docker, and configure Cursor to connect to the local search server. Finally, create a .cursorrules file to guide the search behavior.
Key features
Key features include local code indexing using ChromaDB, semantic search capabilities, easy setup with Docker, and integration with Cursor for enhanced code exploration.
Where to use
undefined
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Content
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
-
Clone and enter the repository:
git clone <repository-url> cd cursor-local-indexing -
Create a
.envfile by copying.env.example:cp .env.example .env -
Configure your
.envfile:PROJECTS_ROOT=~/your/projects/root # Path to your projects directory FOLDERS_TO_INDEX=project1,project2 # Comma-separated list of folders to indexExample:
PROJECTS_ROOT=~/projects FOLDERS_TO_INDEX=project1,project2 -
Start the indexing server:
docker-compose up -d -
Configure Cursor to use the local search server:
Create or edit~/.cursor/mcp.json:{ "mcpServers": { "workspace-code-search": { "url": "http://localhost:8978/sse" } } } -
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.
- 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>
- Start using the Cursor Agent mode and see it doing local vector searches!
Dev Tools Supporting MCP
The following are the main code editors that support the Model Context Protocol. Click the link to visit the official website for more information.
