How to Use the Yelp Fusion MCP in LangChain

Q: Can I check job types using Yelp Fusion within LangChain?

Yep. You run getjobs, providing a natural language query like 'HVAC' or 'plumbing.' The resulting list of specific job categories becomes the output that your next step in the chain can process.

Q: If I need to find event details using Yelp Fusion, does LangChain support it?

Absolutely. You use geteventdetails after an initial search via searchevents. This means your agent doesn't just get a link; it gets all the rich data needed for deeper processing.

Q: What if I need to compare business details? Can LangChain help?

You can run getbusinessdetails multiple times and aggregate those JSON outputs. The power of the MCP Server means you treat these results like sequential data points, comparing them step by step.

Q: Does Yelp Fusion allow me to get review excerpts using LangChain?

Yes. You can call getbusinessreviews and it returns up to three key review excerpts for a business. This specific data type is perfect for passing into an LLM prompt for immediate summarization.

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Connect Yelp Fusion MCP to LangChain

Create your Vinkius account to connect Yelp Fusion to LangChain 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 Yelp Fusion with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Multi-Step Business Discovery

Need to find a business and then know if it's worth checking out? You can chain these calls. First, run `search_businesses` to get IDs for what you're looking for. Next, feed those results into `get_business_details`. Your agent uses the output from the search step as input here, letting you build a solid, traceable workflow.

Analyzing Local Events and Jobs

Planning a trip requires more than just finding a hotel. Combine `get_featured_event` with `search_events`. This lets your agent check the local vibe before booking anything. If that's not enough, you can pivot and run `get_jobs`, specifying things like 'plumbing.' You’ll get a full picture of what services are active in the area.

Deep Dive into Service Offerings

Your agent needs to verify if a business actually supports a service before recommending it. Use `get_service_offerings` on an identified business. This tool lists both active and eligible services, giving you precise data points. It's much better than just reading the main description; it shows what they *can* do.

Setup guide

Set up Yelp Fusion MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Yelp Fusion tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "yelp-fusion-mcp": {
        "transport": "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,
    )
    result = await agent.ainvoke({
        "messages": "List recent Yelp Fusion transactions"
    })
    print(result["messages"][-1].content)

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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 Yelp Fusion. 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 Yelp Fusion MCP in LangChain

You build a graph that routes the data. For example, an agent can first use `search_businesses`, and then pass the resulting list to both `get_business_reviews` and `get_business_insights` simultaneously for parallel analysis.

Yep. You run `get_jobs`, providing a natural language query like 'HVAC' or 'plumbing.' The resulting list of specific job categories becomes the output that your next step in the chain can process.

Absolutely. You use `get_event_details` after an initial search via `search_events`. This means your agent doesn't just get a link; it gets all the rich data needed for deeper processing.

You can run `get_business_details` multiple times and aggregate those JSON outputs. The power of the MCP Server means you treat these results like sequential data points, comparing them step by step.

Yes. You can call `get_business_reviews` and it returns up to three key review excerpts for a business. This specific data type is perfect for passing into an LLM prompt for immediate summarization.

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