NYTimes Article Search MCP Server

The NYTimes Article Search MCP server gives AI assistants a lightweight, focused way to pull recent news content directly from the New York Times. By exposing a single, well‑documented tool that queries the NYT API for articles published in the last 30 days, it removes the friction of manual API integration and lets developers concentrate on building higher‑level reasoning or summarization logic. For teams working with Claude, this means the assistant can instantly answer “What did the NYT say about X last week?” or retrieve a list of relevant stories without additional coding.

At its core, the server implements one tool: . The tool accepts a required parameter and returns an array of article objects, each containing the title, abstract, URL, published date, and author. Because the server handles all HTTP requests to the NYT endpoint, developers no longer need to manage pagination or authentication tokens; the server does that behind the scenes. The tool’s response format is JSON‑friendly, making it easy to pipe results into downstream prompts or further processing steps.

Key features include:

• Time‑bounded search – automatically limits results to the most recent 30 days, ensuring relevance.
• Rich metadata – each article record carries enough information for immediate display or further analysis (e.g., summarization, sentiment).
• Environment‑based API key – the server reads from an environment variable, keeping secrets out of code and configuration files.
• MCP‑standard tooling – the tool follows MCP conventions, so it can be invoked by any compliant client without custom adapters.

Real‑world use cases abound. Journalists building a research assistant can quickly surface background stories for an interview subject. Educators might let students ask the assistant to fetch recent coverage on a historical event, providing authentic primary sources. Business analysts can track competitor mentions or policy changes by searching for relevant keywords and feeding the results into a sentiment model. Because the tool returns structured data, it integrates seamlessly with workflows that chain multiple MCP tools—e.g., search → summarize → translate.

The server’s design offers a distinct advantage: it abstracts away the intricacies of the NYT API while still exposing all useful data fields. Developers can focus on crafting prompts that leverage the search results, confident that the underlying server handles rate limits, error handling, and data formatting. When coupled with MCP’s stdio communication, the server can be launched as a background process and used across multiple AI assistants or even embedded into larger automation pipelines. This makes the NYTimes Article Search MCP server a valuable, plug‑and‑play component for any project that needs timely, authoritative news content.