cursor-local-indexing
ChromaDB-powered local indexing support for Cursor, exposed as an MCP server
Documentation
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-indexing2. Create a `.env` file by copying `.env.example`:
cp .env.example .env3. 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 indexExample:
PROJECTS_ROOT=~/projects
FOLDERS_TO_INDEX=project1,project24. Start the indexing server:
docker-compose up -d5. 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:
For any request, use the @search_code tool to check what the code does.
Prefer that first before resorting to command line grepping etc.8. Start using the Cursor Agent mode and see it doing local vector searches!
Frequently asked questions
What is cursor-local-indexing?
cursor-local-indexing is ChromaDB-powered local indexing support for Cursor, exposed as an MCP server
How do I install cursor-local-indexing?
Open the GitHub repository and follow its README. Most MCP servers are added to your client's MCP config, then called by your agent.
Is cursor-local-indexing open source?
Yes — it is hosted on GitHub at https://github.com/luotocompany/cursor-local-indexing and has 24 stars.
Related MCP tools
🙌 OpenHands: Code Less, Make More for the Model Context Protocol. Enhance AI assistants with powerful integrations. Python-based implementation.
Universal memory layer for AI Agents; Announcing OpenMemory MCP - local and secure memory management. Python-based implementation.
基于大模型搭建的聊天机器人,同时支持 微信公众号、企业微信应用、飞书、钉钉 等接入,可选择ChatGPT/Claude/DeepSeek/文心一言/讯飞星火/通义千问/ Gemini/GLM-4/Kimi/LinkAI,能处理文本、语音和图片,访问操作系统和互联网,支持基于自有知识库进行定制企业智能客服。
Cognee is the open-source AI memory platform for agents. Give your AI agents persistent long-term memory across sessions with a self-hosted knowledge graph engine.
An LLM agent that conducts deep research (local and web) on any given topic and generates a long report with citations. Built for the Model Context Protocol to
🚀 The fast, Pythonic way to build MCP servers and clients Trusted by 19900+ developers. Trusted by 19900+ developers. Trusted by 19900+ developers.
Run your own MCP server? See who uses it and what to fix.
Measure it with TrackMCP