Cursor Chat History Vectorizer & Dockerized Search MCP

What is Cursor Chat History Vectorizer & Dockerized Search MCP?

This server extracts user prompts from Cursor IDE’s local SQLite databases, generates embeddings via a local Ollama instance (using nomic-embed-text), stores them in a LanceDB vector database, and provides a Dockerized FastAPI API for vector similarity search. It is designed for developers who want to make their Cursor chat history searchable for Retrieval Augmented Generation (RAG) or LLM-based analysis.

How to use Cursor Chat History Vectorizer & Dockerized Search MCP?

Two main steps: (1) Run python cursor_history_extractor.py on your host machine to create or update the LanceDB database at ./cursor_chat_history.lancedb. (2) Build the Docker image with docker build -t cursor-chat-search-api . and run the container with docker run -p 8001:8001 -v /absolute/path/to/cursor_chat_history.lancedb:/data/cursor_chat_history.lancedb -e OLLAMA_HOST="http://host.docker.internal:11434" cursor-chat-search-api. The search API will be accessible at http://localhost:8001.

Key features of Cursor Chat History Vectorizer & Dockerized Search MCP

• Extracts user prompts from Cursor’s state.vscdb files
• Generates embeddings using local Ollama (nomic-embed-text)
• Stores vectors and metadata in LanceDB database
• Provides Dockerized FastAPI search server
• Supports vector similarity search via POST /search_chat_history
• Includes health check endpoint (GET /health)

Use cases of Cursor Chat History Vectorizer & Dockerized Search MCP

• Search past Cursor conversations to quickly recall previous solutions
• Feed relevant chat history into LLMs for context-aware code assistance
• Analyze coding patterns by querying vectorized user prompts

FAQ from Cursor Chat History Vectorizer & Dockerized Search MCP

What dependencies are required to run the extraction script?

Python 3.7+, a running Ollama instance with the nomic-embed-text:latest model pulled, and the Python packages ollama, lancedb, pyarrow, pandas, and python-dotenv (installed via pip install -r requirements.txt). Docker Desktop or Docker Engine is needed for the API container.

Where is the vector database stored?

The extraction script creates the database at ./cursor_chat_history.lancedb on the host machine. Inside the Docker container it is expected at /data/cursor_chat_history.lancedb (mounted via the -v flag).

Does this tool extract AI model responses?

No. Currently only user prompts from the aiService.prompts key are extracted and stored. AI responses and other conversation details are not included.

How can I use a custom Cursor workspace