Cursor Chat History Vectorizer & Dockerized Search MCP

by markelaugust74

4 stars

347 downloads

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GitHub

About

API service to search vectorized Cursor IDE chat history using LanceDB and Ollama

Details

Author: markelaugust74
GitHub stars: 4
Downloads: 347
Categories: Developer Tools, API, Knowledge Base

- 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)

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below

Plugin Name Cursor Chat History Vectorizer & Dockerized Search MCP

Command (node, npx, python, etc.)

Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "cursor chat history vectorizer & dockerized search mcp": {
            "cursor-history-mcp-markelaugust74": {
                "command": "python",
                "args": [
                    "cursor_history_extractor.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "cursor chat history vectorizer & dockerized search mcp": {
            "cursor-history-mcp-markelaugust74": {
                "command": "python",
                "args": [
                    "cursor_history_extractor.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "cursor chat history vectorizer & dockerized search mcp": {
            "cursor-history-mcp-markelaugust74": {
                "command": "python",
                "args": [
                    "cursor_history_extractor.py"
                ]
            }
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "cursor chat history vectorizer & dockerized search mcp": {
            "cursor-history-mcp-markelaugust74": {
                "command": "python",
                "args": [
                    "cursor_history_extractor.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "cursor chat history vectorizer & dockerized search mcp": {
            "cursor-history-mcp-markelaugust74": {
                "command": "python",
                "args": [
                    "cursor_history_extractor.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "cursor chat history vectorizer & dockerized search mcp": {
            "cursor-history-mcp-markelaugust74": {
                "command": "python",
                "args": [
                    "cursor_history_extractor.py"
                ]
            }
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "cursor chat history vectorizer & dockerized search mcp": {
            "cursor-history-mcp-markelaugust74": {
                "command": "python",
                "args": [
                    "cursor_history_extractor.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "cursor chat history vectorizer & dockerized search mcp": {
            "cursor-history-mcp-markelaugust74": {
                "command": "python",
                "args": [
                    "cursor_history_extractor.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "cursor chat history vectorizer & dockerized search mcp": {
            "cursor-history-mcp-markelaugust74": {
                "command": "python",
                "args": [
                    "cursor_history_extractor.py"
                ]
            }
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "cursor chat history vectorizer & dockerized search mcp": {
            "cursor-history-mcp-markelaugust74": {
                "command": "python",
                "args": [
                    "cursor_history_extractor.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "cursor chat history vectorizer & dockerized search mcp": {
            "cursor-history-mcp-markelaugust74": {
                "command": "python",
                "args": [
                    "cursor_history_extractor.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "cursor chat history vectorizer & dockerized search mcp": {
            "cursor-history-mcp-markelaugust74": {
                "command": "python",
                "args": [
                    "cursor_history_extractor.py"
                ]
            }
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "cursor chat history vectorizer & dockerized search mcp": {
            "cursor-history-mcp-markelaugust74": {
                "command": "python",
                "args": [
                    "cursor_history_extractor.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "cursor chat history vectorizer & dockerized search mcp": {
            "cursor-history-mcp-markelaugust74": {
                "command": "python",
                "args": [
                    "cursor_history_extractor.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "cursor chat history vectorizer & dockerized search mcp": {
            "cursor-history-mcp-markelaugust74": {
                "command": "python",
                "args": [
                    "cursor_history_extractor.py"
                ]
            }
        }
    }
}

From the repository

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.

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "cursor chat history vectorizer & dockerized search mcp": {
            "cursor-history-mcp-markelaugust74": {
                "command": "python",
                "args": [
                    "cursor_history_extractor.py"
                ]
            }
        }
    }
}

McpServers

{
    "cursor-history-mcp-markelaugust74": {
        "command": "python",
        "args": [
            "cursor_history_extractor.py"
        ]
    }
}

Cursor Chat History Vectorizer & Dockerized Search MCP

Vectorize your Cursor chat history and serve it via a simple search API. This project provides tools to: 1. Extract chat history from local Cursor IDE data (state.vscdb files within workspace storage). 2. Generate text embeddings for user prompts using a local Ollama instance (nomic-embed-text). 3. Store the extracted prompts and their embeddings in a LanceDB vector database. 4. Include a Dockerized FastAPI application (referred to as an "MCP server" in this context) to search this LanceDB database via a simple API endpoint.

✨ Project Goal

The primary goal is to make your Cursor chat history searchable and usable for Retrieval Augmented Generation (RAG) or other LLM-based analysis by: Converting user prompts into vector embeddings stored efficiently in LanceDB. Providing a simple and accessible API server to perform vector similarity searches against your vectorized history.

🚀 Features

Data Extraction: Scans specified Cursor workspace storage paths for state.vscdb SQLite files. Prompt Extraction: Extracts user prompts from the aiService.prompts key within the database files. Embedding Generation: Uses a locally running Ollama instance to generate embeddings for extracted prompts. Embedding Model: nomic-embed-embed-text:latest (default dimension 768). Vector Database Storage: Stores original text, source file, role, and vector embeddings in a LanceDB database. LanceDB URI: ./cursor_chat_history.lancedb (for the extractor) / /data/cursor_chat_history.lancedb (inside Docker container) Table Name: chat_history Dockerized Search : Includes a Dockerfile to build a container for the Fast search server. Fast Server (main.py): Acts as the "MCP server" for handling search requests. Endpoints: /search_chat_history (POST): Performs vector similarity search. /health (GET): Checks server status and connections (Ollama, LanceDB).

📋 Requirements

For Running the Extraction Script (cursor_history_extractor.py): Python 3.7+ Ollama: Ensure Ollama is installed and running on your local machine. Pull the nomic-embed-text model: ``

bash
    ollama pull nomic-embed-text:latest


   Python Packages: Install required packages:

`bash pip install ollama lancedb pyarrow pandas python-dotenv` File Access: Read access to your Cursor workspace storage directory (default: C:\Users\<name>\AppData\Roaming\Cursor\User\workspaceStorage

).

For Running the Search API (

main.py) via Docker:



   Docker Desktop (Windows/Mac) or Docker Engine (Linux).
   An accessible Ollama instance from the Docker container's network.
   The LanceDB database directory (

./cursor_chat_history.lancedb) already created by the extraction script. ⚙️ Setup & Configuration The process involves two main steps: 1. Run the extraction script to create or update the LanceDB database on your host machine. 2. Build and run the Docker container for the search API, mounting the database created in Step 1. Step 1: Extract & Create Database (Host Machine) 1. Clone/Download the Project:`bash git clone https://github.com/markelaugust74/Cursor-history-MCP.git cd Cursor-history-API`2. Install Python dependencies for the extractor:`bash pip install -r requirements.txt`3. Verify Paths (if necessary): Update the WORKSPACE_STORAGE_PATH variable in cursor_history_extractor.py

 if your Cursor data is not in the default location.
       Ensure you have write permissions in the directory where you run the script, as

./cursor_chat_history.lancedbwill be created here. 4. Ensure Ollama is Running: Start your Ollama server and confirmnomic-embed-text:latest is available (ollama list). 5. Execute the extraction script:`bash python cursor_history_extractor.py`This script will print progress and, if successful, create the./cursor_chat_history.lancedbdirectory containing your vectorized history. Step 2: Build & Run API Docker Container 1. Navigate to the project directory containing theDockerfile, main.py, and the ./cursor_chat_history.lancedbdirectory created in Step 1. 2. Build the Docker image:`bash docker build -t cursor-chat-search-api .`3. Run the Docker container:`bash docker run -p 8001:8001 \ -v /path/to/your/cursor_chat_history.lancedb:/data/cursor_chat_history.lancedb \ -e OLLAMA_HOST="http://host.docker.internal:11434" \ cursor-chat-search-api` -p 8001:8001

: Maps port 8001 on your host machine to port 8001 inside the container (where the FastAPI app runs).

-v /path/to/your/cursor_chat_history.lancedb:/data/cursor_chat_history.lancedb: This is CRUCIAL. Replace /path/to/your/cursor_chat_history.lancedb with the absolute path on your host machine to the cursor_chat_history.lancedb directory created by the extraction script. This mounts your host database into the container at /data/cursor_chat_history.lancedb, the location expected by main.py. (Use forward slashes for paths even on Windows in Docker commands, or ensure proper escaping/configuration). -e OLLAMA_HOST="...": Sets the OLLAMA_HOST environment variable inside the container. http://host.docker.internal:11434

 is common for Docker Desktop to reach the host. For Linux, you might need a different approach (e.g., host network mode, or using the host's IP accessible from the container).
4.  The FastAPI application (your "MCP server") should now be running and accessible via

http://localhost:8001

.

▶️ How to Run


The overall workflow is:

1.  Execute

python cursor_history_extractor.py periodically on your host machine to create/update ./cursor_chat_history.lancedb

.
2.  Run the

docker run command from the project root (where the .lancedb

 directory exists) to start the API server. This server will access the LanceDB database via the volume mount.

📁 Output

./cursor_chat_history.lancedb: A directory created by the extraction script containing the LanceDB vector database. Its schema includes vector (float list), text (string), source_db (string), and role(string).

   A running API server inside the Docker container, accessible externally via the mapped port (default 8001), providing the defined API endpoints.

🔌 API Usage


Once the Docker container is running and the API is accessible (e.g., at

http://localhost:8001

), you can interact with it.

`Health Check (`GET /health`)`



Checks the server's status and its connections to Ollama and LanceDB.

bash
curl http://localhost:8001/health

Example Response:

{ "status": "healthy", "ollama_connection": "Connected", "lancedb_connection": "Connected and table open" }

Search History (POST /search_chat_history)

Performs a vector similarity search against the LanceDB chat history. curl -X POST http://localhost:8001/search_chat_history \ -H "Content-Type: application/json" \ -d '{"query_text": "How do I use Python with data analysis?", "top_k": 5}'

🔍 Direct Database Inspection

After running the cursor_history_extractor.py script, you can inspect the LanceDB database file directly using Python (outside the Docker container). import lancedb import ollama # Required if you want to perform searches

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About

Details

Jump to

Setting up with Highlight

From the repository

Claude Desktop / Cursor

McpServers

Cursor Chat History Vectorizer & Dockerized Search MCP

✨ Project Goal

🚀 Features

📋 Requirements

⚙️ Setup & Configuration

Step 1: Extract & Create Database (Host Machine)

Step 2: Build & Run API Docker Container

▶️ How to Run

📁 Output

🔌 API Usage

Health Check (GET /health)

Example Response:

Search History (POST /search_chat_history)

🔍 Direct Database Inspection

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`Health Check (`GET /health`)`