Integrations
Enables configuration through environment variables stored in a .env file, specifically for storing and accessing the New York Times API key.
Provides tools for searching New York Times articles from the last 30 days based on keywords, returning article details including title, abstract, URL, published date, and author.
NYTimes Article Search MCP Server
This is a TypeScript-based MCP server that allows searching for New York Times articles from the last 30 days based on a keyword. It demonstrates core MCP concepts by providing:
- Tools for searching articles
- Integration with the New York Times API
Features
Tools
search_articles
- Search NYTimes articles from the last 30 days based on a keyword- Takes
keyword
as a required parameter - Returns a list of articles with title, abstract, URL, published date, and author
- Takes
Development
Install dependencies:
Build the server:
For development with auto-rebuild:
Debugging
Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the MCP Inspector, which is available as a package script:
The Inspector will provide a URL to access debugging tools in your browser.
Installation
Installing via Smithery
To install NYTimes Article Search for Claude Desktop automatically via Smithery:
Manual Installation
To use with Claude Desktop, add the server config:
On MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json
Environment Variables
Create a .env
file in the root of your project and add your New York Times API key:
Running the Server
After building the project, you can run the server with:
The server will start and listen for MCP requests over stdio.
License
This project is licensed under the MIT License.
You must be authenticated.
remote-capable server
The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.
This is a TypeScript-based MCP server that allows searching for New York Times articles from the last 30 days based on a keyword.
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