New York Times MCP Server for LangChain 9 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect New York Times through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with MultiServerMCPClient({
"new-york-times": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using New York Times, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About New York Times MCP Server
Connect the New York Times API to any AI agent and unlock access to over 170 years of journalism — including breaking news, historical archives, best-seller lists, and cultural reviews.
LangChain's ecosystem of 500+ components combines seamlessly with New York Times through native MCP adapters. Connect 9 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
What you can do
- Top Stories — Get the latest top stories for any section (World, Politics, Tech, Sports, etc.)
- Article Search — Search the complete archive from 1851 to the present day with keywords and date filters
- Most Popular — See what readers are emailing, sharing, and viewing the most
- Best-Seller Lists — Retrieve current and historical book best-seller lists
- Movie Reviews — Access thousands of movie reviews and critic summaries
- Section Discovery — List all available sections and topics covered by the NYTimes
The New York Times MCP Server exposes 9 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect New York Times to LangChain via MCP
Follow these steps to integrate the New York Times MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 9 tools from New York Times via MCP
Why Use LangChain with the New York Times MCP Server
LangChain provides unique advantages when paired with New York Times through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents — combine New York Times MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across New York Times queries for multi-turn workflows
New York Times + LangChain Use Cases
Practical scenarios where LangChain combined with the New York Times MCP Server delivers measurable value.
RAG with live data: combine New York Times tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query New York Times, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain New York Times tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every New York Times tool call, measure latency, and optimize your agent's performance
New York Times MCP Tools for LangChain (9)
These 9 tools become available when you connect New York Times to LangChain via MCP:
get_archive
Get all articles for a specific month
get_book_lists
"list_name_encoded" is the list slug (e.g., "hardcover-fiction"). Optional date is YYYY-MM-DD. Get current or historical best-seller lists
get_most_emailed
Period can be 1, 7, or 30 days. Get the most emailed articles for a specific period
get_most_shared
Period can be 1, 7, or 30 days. Get the most shared articles on social media
get_most_viewed
Get the most viewed articles
get_movie_reviews
Optional "query" filters by movie title. Search for movie reviews in the NYTimes archive
get_sections
List all available news sections
get_top_stories
g., home, world, politics, technology, sports). Use get_sections to see available options. Get top stories for a specific section
search_articles
Use "q" for keywords, "begin_date" and "end_date" for date ranges (YYYYMMDD), and "sort" for "newest", "oldest", or "relevance". Search for articles using keywords, date ranges, and sorting
Example Prompts for New York Times in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with New York Times immediately.
"Show me today's top world news."
"What is the #1 Hardcover Fiction book this week?"
"Find movie reviews for 'The Godfather'."
Troubleshooting New York Times MCP Server with LangChain
Common issues when connecting New York Times to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersNew York Times + LangChain FAQ
Common questions about integrating New York Times MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
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Connect New York Times to LangChain
Get your token, paste the configuration, and start using 9 tools in under 2 minutes. No API key management needed.