NY Times MCP. Access 170+ Years of Archived Global Journalism Data
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New York Times MCP Server gives your agent access to over 170 years of journalism. Search articles by keyword and date range, check top stories for specific sections, track historical best-seller lists, or find movie reviews from the archives.
It's a deep data pull, not real-time browsing.
What your AI agents can do
Get archive
Retrieves all articles published for an entire calendar month.
Get book lists
Gets current or historical best-seller lists; requires a list slug and optional date.
Get most emailed
Returns the articles that were most emailed within a specified period (1, 7, or 30 days).
You input keywords and a date range, and your agent returns all matching articles from the NYT's entire database.
Your agent fetches the most recent top stories for any major section (e.g., Politics or World).
You ask to see which articles were shared, emailed, or viewed the most during a specific 1-, 7-, or 30-day window.
Your agent pulls either current or historical best-seller rankings for specified genres like hardcover fiction.
You can search the archives specifically for film criticism, optionally filtering by a movie title.
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New York Times MCP Server: 9 Tools for Content Retrieval
These tools let your agent access specific data points—from historical articles to current section headlines—across the entire NYT archive.
019d75e1get archive
Retrieves all articles published for an entire calendar month.
019d75e1get book lists
Gets current or historical best-seller lists; requires a list slug and optional date.
019d75e1get most emailed
Returns the articles that were most emailed within a specified period (1, 7, or 30 days).
019d75e1get most shared
Gets articles that saw the highest volume of social media sharing over one, seven, or thirty days.
019d75e1get most viewed
Retrieves a list of the most viewed articles published by the NYT.
019d75e1get movie reviews
Searches for film criticism within the archives, optionally filtering results by movie title.
019d75e1get sections
Lists every available news section and topic covered by the New York Times.
019d75e1get top stories
Fetches the most recent top headlines for a specific, designated news section (e.g., 'World' or 'Tech').
019d75e1search articles
Searches articles using keywords ('q'), sets a date range (YYYYMMDD), and sorts by relevance, oldest, or newest.
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What you can do with this MCP connector
New York Times MCP Server: News & Archive Data. You're getting your agent access to over 170 years of journalism—that’s deep data, man. This ain't real-time browsing; you'll pull historical context, best-seller lists, and film criticism straight into your workflow.
Searching the Archives: If you need to dig deep, use search_articles to find articles across keywords (q) within a specific date range (YYYYMMDD), letting you sort by relevance, oldest content, or newest drops. For massive data pulls covering an entire calendar month, run get_archive. You can also check out what sections the paper covers at any time using get_sections.
Top Stories and Current Sections: Want to know what's hot right now? Your agent fetches recent top headlines for a specific major section—think 'World' or 'Tech'—using get_top_stories. This tool lets you see the biggest stories hitting any designated news category.
Analyzing Content Popularity: You can track how articles perform over time using three different metrics. To find out what got the most attention, call get_most_viewed to retrieve a list of the highest-seen pieces. If email traction is key, use get_most_emailed; for social media buzz, check get_most_shared. All three metrics let you specify a period: one day (1), seven days (7), or thirty days (30).
Media and Culture Deep Dives: For film buffs, you can run get_movie_reviews to search the archives specifically for film criticism; this tool lets you optionally filter results by a specific movie title. To check out what's selling, use get_book_lists; you gotta provide a list slug and can narrow it down further with an optional date parameter to get current or historical best-seller rankings.
Tool Summary: You run search_articles using keywords and dates; you call get_top_stories for section headlines; you use the popularity tools (get_most_emailed, get_most_shared, get_most_viewed) to measure engagement over specific time windows; you check out monthly data with get_archive; you find movie reviews via get_movie_reviews; and you grab book rankings using get_book_lists.
How NY Times MCP Works
- 1 Subscribe to this server and provide your NYTimes Developer API Key.
- 2 Your AI client builds a specific function call (e.g.,
search_articleswith 'climate change' and date filters). - 3 The server executes the tool against the NYT archive and returns structured data directly to your agent.
The bottom line is you get highly specified, historical journalistic data without running a web scrape or building complex database connectors.
Who Is NY Times MCP For?
This server targets researchers and content professionals who need deep context. If your job requires knowing not just what happened yesterday, but how it was covered by the media over decades, this is for you. It's built for people whose work depends on historical data retrieval.
Tracks how a specific social or political topic (like climate change) was reported and framed by the NYT over 20 years, using search_articles with date filters.
Needs background context for a current story. Runs get_sections to map out related topics and uses get_archive to find coverage from 10 years ago.
Monitors cultural trends by checking historical best-seller lists using get_book_lists or tracking which articles are most shared over a quarter.
What Changes When You Connect
- Tracks media coverage history: Use
search_articlesto see how a political event was reported in the year it happened versus its coverage five years later. This is vital for academic comparison. - Quantifies cultural trends: Running
get_book_listslets you analyze the longevity of certain genres or authors, far beyond just checking today's chart toppers. - Maps news sections instantly: Call
get_sectionsto discover every niche topic the paper covers. This prevents guesswork and ensures your agent knows all possible data endpoints. - Identifies viral content patterns: Use
get_most_shared,get_most_emailed, orget_most_viewedto understand what type of story generates maximum buzz, regardless of current headlines. - Reviews film history with precision: The
get_movie_reviewstool lets you pull classic film criticism and compare it against modern takes on the same title.
Real-World Use Cases
Tracking a politician's public image
A political data analyst needs to track how coverage of Candidate X changed between 2015 and 2020. They use search_articles with the candidate's name as the keyword ('q') and set the start/end dates. This provides a direct, quantifiable view of the media narrative shift.
Comparing book market cycles
A publishing executive wants to know if 'historical fiction' is consistently popular. They use get_book_lists with historical slugs and dates, comparing multiple years' data points to model potential sales curves.
Researching the origin of a topic
A journalist wants context on 'quantum computing.' Instead of searching general web sources, they call get_top_stories for the 'Technology' section and then use search_articles to pinpoint when the concept was first covered in depth.
Understanding a cultural moment
A media historian wants to know what people were talking about during the 2008 financial crisis. They call get_most_shared for that period, combined with searching popular articles, to get a snapshot of public engagement.
The Tradeoffs
Trying to find today's trending topics
Asking the agent, 'What are people reading right now?' or relying on general web search results.
→
You must use get_top_stories combined with get_sections. This limits your query to official NYT categories and provides actionable headlines.
Searching by vague date ranges
Asking for 'articles from the last month' without providing specific dates, leading to incomplete or inaccurate results.
→
Always use search_articles and provide precise YYYYMMDD formatted start and end dates. If you need a whole month, use get_archive first.
Assuming all data is in one place
Asking for both best-seller lists and top stories using only a general search tool.
→
You have to call the specific tools: use get_book_lists for books, and then use get_top_stories or search_articles for news.
When It Fits, When It Doesn't
Use this server if your primary need is deep historical data retrieval from a single, trusted source. You need to know how something was covered over time—whether it's a political event (search_articles), an industry trend (get_top_stories), or cultural popularity (get_book_lists).
Don't use this if: 1) you need real-time, minute-by-minute updates from today’s live site (use a dedicated live news API); 2) your data needs to cross multiple different media outlets (you'll need an aggregation layer); or 3) you are looking for opinionated commentary that isn't explicitly labeled as criticism (get_movie_reviews handles this niche well, but general opinion is outside its scope).
Always map your query back to a specific tool: If the data point involves keywords and dates, use search_articles. If it’s about current headlines in one area, use get_top_stories.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by New York Times. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 9 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Sifting through decades of news archives is a massive headache.
Manually researching a topic that spanned 30 years means logging into multiple databases, running different keyword searches, and manually checking date filters. You end up with dozens of separate spreadsheets, none of which give you a clear picture of how coverage changed over time.
With this MCP server, your agent runs `search_articles` once. It handles the date range formatting and returns structured data covering that entire 30-year window—all in one clean output.
New York Times MCP Server: Get structured media context.
Before this, finding out what was most popular required checking three different dashboards: the 'most shared' counter, the 'most emailed' list, and the current top stories page. It was a multi-step manual process that often missed data points.
Now, you call `get_most_shared` or `get_top_stories`. The server pulls the precise metrics—whether it’s social traction or editorial importance—and delivers clean, structured JSON immediately.
Common Questions About NY Times MCP
How do I search for articles before 2000 using `search_articles`? +
You use the date parameters in the YYYYMMDD format. Set your 'begin_date' to the earliest year you need (e.g., 18510101) and set your 'end_date' accordingly. The tool handles the date range correctly.
Can I find out which sections exist using `get_sections`? +
Yes, calling get_sections provides a comprehensive list of all available news topics and categories, allowing you to target your queries precisely afterward.
Which tool should I use if I only want today's headlines for sports? +
You need to call get_top_stories. You must first ensure 'Sports' is a valid section by running get_sections, then pass that exact section name to the top stories function.
What if I want to know what was popular 7 days ago? +
Use get_most_shared or get_most_emailed. Both tools accept '7' as a valid period argument, giving you the metrics for that specific timeframe.
What key information do I need before running any tool, like `get_top_stories`? +
You must provide your NYTimes Developer API Key during setup. Your AI client uses this key to authorize every request against the server.
How do I manage my usage limits when using `search_articles` or other data tools? +
The NYTimes API provides a generous limit of requests per day. The server monitors consumption automatically, and your AI client handles rate limiting when necessary.
When using `get_book_lists`, how do I specify if I want historical data versus current lists? +
You pass the desired date in YYYY-MM-DD format as an optional argument. If you omit the date, it returns the most current list available.
What should happen if I use `search_articles` with conflicting or impossible date ranges? +
The API will return a specific error message detailing the conflict. Always verify that your beginning and ending dates follow the YYYYMMDD format and are chronologically correct.
How far back does the NYTimes archive go? +
The article search API provides access to articles dating back to 1851. It is one of the most comprehensive historical newspaper archives available.
Can I get the actual text of the articles? +
The API provides the headline, abstract (summary), snippet, and URL to the full article on NYTimes.com. Full text is not included in the API response but can be accessed via the provided link.
What sections are available for Top Stories? +
You can access almost any section of the NYTimes, including home, world, politics, business, technology, sports, arts, health, and science. Use the get_sections tool to see the full list.
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