US Stock Data MCP Server
Description
# US Stock Data MCP Server A Model Context Protocol (MCP) server designed for accessing and updating US stock historical price data. ## Features - Local Data Storage: Store stock data in CSV format locally for quick access - Automatic Updates: Support for automatic stock data…
About
# US Stock Data MCP Server A Model Context Protocol (MCP) server designed for accessing and updating US stock historical price data. ## Features - Local Data Storage: Store stock data in CSV format locally for quick access - Automatic Updates: Support for automatic stock data updates from Yahoo Finance - Safe Data…
Details
- Author
- mingyaw
- Downloads
- 332
- Categories
- Finance
Jump to
- Local CSV storage for quick access
- Automatic data updates from Yahoo Finance
- Safe atomic writes using temporary files
- Customizable start date for updates
- Duplicate data automatically handled
- 5-second delay between updates to avoid rate limits
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
US Stock Data MCP ServerCommand (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
From the repository
Install Python 3.x, clone the repository, and run pip install -r requirements.txt. Start the server with python server.py. The default data storage path is ~/Library/Application Support/us-market-data/data, which can be customized using the US_STOCK_DATA_DIR environment variable. Use the MCP tools get_local_stock_data and update_stock_data to retrieve and update historical data.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"us stock data mcp server": {
"us_stock_mcp_server": {
"command": "python",
"args": [
"server.py"
]
}
}
}
}McpServers
{
"us_stock_mcp_server": {
"command": "python",
"args": [
"server.py"
]
}
}US Stock Data MCP Server
A Model Context Protocol (MCP) server designed for accessing and updating US stock historical price data.
Features
- Local Data Storage: Store stock data in CSV format locally for quick access
- Automatic Updates: Support for automatic stock data updates from Yahoo Finance
- Safe Data Writing: Use temporary files to ensure atomic and secure data writing
- Flexible Time Range: Customizable start date for data updates
Installation
1. Ensure Python 3.x is installed
2. Clone this repository
3. Install dependencies:
pip install -r requirements.txt
Usage
1. Start the Server
python server.py
The default data storage path after server startup is: ~/Library/Application Support/us-market-data/data
You can customize the data storage path using the US_STOCK_DATA_DIR environment variable.
2. Available Features
MCP Tools
1. get_local_stock_data
- Function: Retrieve local stock historical data
- Parameters:
- symbol: Stock symbol, e.g., 'AAPL', 'MSFT'
2. update_stock_data
- Function: Update stock data
- Parameters:
- symbol: Stock symbol, e.g., 'AAPL', 'MSFT'
- start_date: Start date in YYYY-MM-DD format, defaults to 2015-01-01
MCP Resources
- Resource URI: usstock://{symbol}/historical
- Function: Provide local US stock historical price data
- Parameters:
- symbol: Stock symbol
Data Format
The stored stock data includes the following fields:
- Date: Trading date
- Open: Opening price
- High: Highest price
- Low: Lowest price
- Close: Closing price
- Volume: Trading volume
Dependencies
- mcp: MCP protocol implementation
- pandas: Data processing and analysis
- yfinance: Yahoo Finance data retrieval
- pydantic: Data validation and settings management
Notes
1. Duplicate data is automatically handled during updates, keeping the latest records
2. A 5-second delay is implemented between update operations to avoid frequent API requests
3. All data operations include error handling to ensure service stability
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