How to Use the New York Times MCP in LlamaIndex

Q: Can LlamaIndex query past New York Times search results?

Yes, you can store the outputs of searcharticles in a persistent LlamaIndex index. This allows your LlamaIndex agent to run semantic queries over previously fetched New York Times search results.

Index New York Times articles directly into your LlamaIndex vector stores for semantic retrieval with this MCP Server.

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Connect New York Times MCP to LlamaIndex

Create your Vinkius account to connect New York Times to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup New York Times with LlamaIndex

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Index live New York Times feeds into LlamaIndex

The `get_top_stories` tool serves as a live data loader that feeds raw New York Times editorial content straight into your LlamaIndex document pipeline. Your LlamaIndex indexing pipeline processes these New York Times headlines immediately, turning current news into searchable vector embeddings. This method prevents your LlamaIndex RAG application from answering questions with outdated New York Times information. Grounding your LlamaIndex queries in the latest New York Times articles eliminates the hallucination risks common in static models.

Build searchable LlamaIndex archives from historical news

The `get_archive` tool lets your LlamaIndex agent ingest entire months of historical New York Times coverage to build a specialized knowledge base. This MCP Server allows you to parse thousands of New York Times articles, chunk them, and store them in a local LlamaIndex vector database. Users can then query this historical New York Times index using natural language within LlamaIndex. The LlamaIndex query engine retrieves the exact paragraphs needed from the New York Times archive, referencing the original publication date and section metadata.

Analyze trending New York Times topics in LlamaIndex

The `get_most_shared` tool extracts viral New York Times article metadata so your LlamaIndex pipeline can index what the public is talking about. This lets your LlamaIndex agent run semantic searches over trending New York Times news to identify shifting cultural narratives. Combining this with `get_movie_reviews` allows your LlamaIndex application to build a complete cultural index of New York Times critiques. The LlamaIndex framework treats these New York Times reviews as structured documents, making them instantly queryable alongside your existing files.

Setup guide

Set up New York Times MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all New York Times MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to New York Times tools.",
)
response = await agent.run("List recent New York Times data")

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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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Common questions about New York Times MCP in LlamaIndex

You use the LlamaIndex MCP tool spec to connect to the server and pull New York Times articles via `get_archive`. The LlamaIndex framework then chunks the text and indexes it directly into your vector database.

Yes, you can store the outputs of `search_articles` in a persistent LlamaIndex index. This allows your LlamaIndex agent to run semantic queries over previously fetched New York Times search results.

You convert the MCP Server tools using the LlamaIndex tool adapter and pass them to a function-calling agent. The LlamaIndex agent then decides when to call `get_top_stories` to update its internal knowledge base with current New York Times stories.

Yes, you can use the allowed tools list in your configuration to restrict your LlamaIndex agent to only see `get_movie_reviews`. This limits the agent's scope to cinematic New York Times content while ignoring other endpoints.

Your local LlamaIndex vector indices and New York Times API keys are stored within your own infrastructure, while the server runs in a secure, zero-trust sandbox. No New York Times article texts or search parameters are cached or stored on our servers.

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