New York Times MCP. Search 170+ Years of Global Journalism Data
New York Times MCP connects your AI agent directly to over 170 years of global journalism. Search archives from 1851 forward, track historical trends, pull today's top stories by section (Politics, World, Tech), and access best-seller lists and film reviews—all in one place.
Give Claude and any AI agent real-world access
Pull articles and reports from specific years to track how a topic or event was covered over decades.
Determine which stories were the most viewed, shared on social media, or emailed during any 1-, 7-, or 30-day period.
Use keywords and precise date ranges (YYYYMMDD) to find specific articles across the entire publication history.
Get the day's top stories instantly, filtered by major sections like World, Politics, or Technology.
Retrieve current and historical book best-seller lists or search for movie reviews using specific titles.
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What AI agents can do with New York Times: 9 Available Tools
These tools allow your agent to perform specific actions, like retrieving top stories for a section or searching the entire historical archive by keywords and date.
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Start using New York Times MCPGet Archive
Retrieves every article published during a specified calendar month.
Get Book Lists
Gets current or historical best-seller lists for different book categories.
Get Most Emailed
Finds the articles that were most shared via email over a 1, 7, or 30 day period.
Get Most Shared
Retrieves articles that saw the highest social media sharing counts in the last 1...
Get Most Viewed
Identifies the most read and viewed articles across all sections.
Get Movie Reviews
Searches the archive specifically for film critiques, allowing filtering by movie title.
Search Articles
Performs keyword searches across the entire database, letting you filter by date range and sort order.
Get Sections
Lists every available topic or news section covered by the New York Times.
Get Top Stories
Gets today's top headline stories for a specific defined section, like World or...
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Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
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The endless cycle of context switching
Right now, if your research requires knowing how a modern issue relates to historical coverage, you spend hours hopping between databases. You copy keywords into one search engine for 1950 data, open another terminal for best-seller lists from the same era, and finally manually check a third source just to validate the political context.
With this MCP, your agent handles all of that in one go. You ask a single question—like 'How was the Cold War covered in both 1962 and 1985?'—and you get the compiled, sourced answers directly from the archive.
Accessing Historical Context with `search_articles`
Previously, comparing coverage across different years meant manually checking archives month by month. You’d use one tool for one date range and then start the whole tedious process over again for the next time period.
Now you define a single search using `search_articles`, providing keywords and multiple dates in one command. It builds the entire timeline of coverage for you.
What New York Times MCP does for your AI
Think about the sheer volume of information sitting across decades: breaking news reports, cultural critiques, market shifts. This MCP gives your AI agent direct access to that archive. Instead of relying on summaries or limited databases, you can query the full scope of modern journalism, cross-referencing topics and dates from 1851 right up to today.
It’s more than just reading headlines; it’s historical context in real time. You can pull top stories for a specific section, narrow your search by keywords and date ranges, or see what was most shared across social media on any given day. The Vinkius catalog makes this massive dataset available to every MCP-compatible client you use.
Whether you're writing an academic paper that needs background coverage from 1920, researching the cultural impact of a modern tech policy, or just checking out what books were trending last month, this connector handles it. It brings world-class journalism into your agent's hands.
019d75e1-b64d-70ce-8dc4-8fd753bb89e0 How to set up New York Times MCP
The bottom line is that your agent stops needing general internet searches and starts asking targeted questions against a verified, deep repository of journalism.
Subscribe to this MCP on Vinkius and enter your New York Times Developer API Key.
Your AI agent uses the connection credentials to initiate a query, specifying criteria like keywords, date ranges, or sections (e.g., 'World Politics').
The system returns structured data containing article summaries, full text snippets, and related contextual information from the archive.
Who uses New York Times MCP
Anyone whose job involves synthesizing information across time periods. If you're tired of stitching together research from five different sources just to build context, this is for you.
Building a paper that requires comparing the media coverage of civil rights in 1960 versus 2020. You use the archive search tools to find direct comparisons.
Writing an article on a current event and need background context, finding related coverage or historical parallels from previous decades for depth.
Tracking how consumer sentiment regarding technology has shifted over time by cross-referencing top stories in the Tech section across different years.
Benefits of connecting New York Times MCP
Contextual depth: Use the search_articles tool to find articles from specific date ranges, allowing you to compare how a single event was reported decades apart.
Trend Spotting: Check what's popular right now. The get_most_emailed, get_most_shared, and get_most_viewed tools tell you exactly where the public attention is focused in any given period.
Broad Coverage: Need to know about books or films? You can check out best-seller lists using get_book_lists or find movie critiques with get_movie_reviews, all from one source.
Targeted News Retrieval: Don't waste time sifting through everything. Use get_top_stories to get the absolute latest headlines for a specific topic, like Politics or Tech.
Structural Research: Start by running get_sections to map out every possible content area before you even start searching for keywords.
New York Times MCP use cases
Tracking Policy Shifts Over Time
A historian needs to understand how the coverage of climate change shifted between 1980 and 2000. They use search_articles with a date range, finding direct comparison pieces that show the evolution of public discourse.
Writing an Industry Deep Dive
A journalist needs to write about a tech company's rise. Instead of guessing, they use get_top_stories for 'Technology' and then follow up with search_articles to pull specific early coverage dates, building a fully sourced narrative.
Monitoring Viral Topics
A marketing team wants to know what topics generate buzz. They run get_most_shared for the last 7 days, immediately identifying which cultural or political subjects are currently dominating social conversation.
New York Times MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Searching everything with one keyword
Asking your agent: 'Tell me about global warming.' This is too broad and returns a mix of modern, historical, and tangential articles without focus.
Narrow the scope. Use search_articles and specify both keywords (e.g., 'Paris Agreement') AND a precise date range (e.g., 201509). This forces the agent to find highly relevant coverage.
Assuming only today's news matters
Asking: 'What were people talking about in sports?' without limiting the time frame, resulting in a massive data dump that is unusable.
Specify your intent. If you want historical context, use get_archive to define the exact month and year you are interested in.
Mixing content types randomly
Asking: 'What was trending last week regarding books and politics?' This mixes unrelated data streams.
Handle them separately. First, use get_book_lists for the book info; then, check political coverage using get_top_stories or search_articles.
When to use New York Times MCP
Use this MCP if your goal is deep historical research or tracking cultural trends over time. You need verifiable context from a massive, established journalistic archive. Don't use it if you are looking for highly niche, non-journalistic data like real-time stock ticker prices or private internal company documents; those require specialized databases. If you just need today's breaking headlines without any historical depth, get_top_stories is perfect. But if the 'why' and 'how it started' are as important as the 'what happened yesterday,' this MCP gives you the necessary decades-long view.
Frequently asked questions about New York Times MCP
Can I use New York Times MCP to find articles about a specific month? +
Yes, you can use the get_archive tool. This function retrieves all published articles within an entire calendar month for comprehensive coverage.
Does New York Times MCP cover more than just news stories? +
Absolutely. Beyond top stories and archives, this MCP also includes tools for best-seller lists using get_book_lists and movie reviews via get_movie_reviews.
How do I find out what was popular last year? +
You can use the trending tools. Run get_most_shared or get_most_emailed, specifying a 30-day period within the past year to pinpoint peak interest.
Can I search for articles using keywords and dates in New York Times MCP? +
Yes, that's exactly what search_articles is for. You provide your keywords and define a precise date range (YYYYMMDD) to focus your search.
What kind of sections are available in the New York Times MCP? +
You use the get_sections tool first. This lists all currently active topics, ensuring you know exactly which categories (like World or Sports) you can pull top stories from.