How to Use the Yelp Fusion MCP in CrewAI

Q: How can I get rich Yelp Fusion data using CrewAI?

Assign a 'Data Retrieval Agent' to use getbusinessdetails. This agent pulls comprehensive information for any business ID. The result is then passed to another specialized agent for summarization.

Q: What is the best way to search for multiple businesses with Yelp Fusion and CrewAI?

You'll create a 'Search Team.' One agent runs searchbusinesses initially. The next agent iterates through the results, calling getreviews on each one to compile a full report.

Q: Can Yelp Fusion handle job searches using CrewAI?

Yes. An 'Operations Agent' can use getjobs, taking natural language input like 'plumbing.' It then uses the results to cross-reference against existing businesses via matchbusiness.

Q: If I need event information, how does Yelp Fusion work with CrewAI?

A dedicated agent can run searchevents. If that yields too many results, a 'Moderator Agent' can then call getfeaturedevent to narrow the focus and present a single best option.

Run specialized Yelp Fusion operations using CrewAI multi-agent teams.

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Create your Vinkius account to connect Yelp Fusion to CrewAI 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 CrewAI

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Autonomous Business Research with MCP Server

You set up a 'Research Agent' that uses `search_businesses` to find potential businesses. This agent passes the results to an 'Analysis Agent,' which then calls `get_business_insights` for deeper analysis. This team structure allows one agent to search and another to interpret complex data, like monthly insights from `get_business_insights`, without needing human intervention.

Coordinating Local Search with Yelp Fusion and CrewAI

A 'Moderator Agent' can oversee a local discovery process. It might start by asking the user to search for events using `search_events`. If that fails, it delegates the task to an 'Action Agent' that tries searching by phone number via `search_by_phone`. This role-based specialization ensures the correct tool is called based on the failure or success of the previous step.

Handling Service Discovery with MCP Server

One agent can use `get_categories` to understand all available business types. A second agent then uses that list to guide a query run by `get_service_offerings`, finding active services for a specific location. This multi-agent approach is ideal for building sophisticated, self-correcting service recommendation systems.

Setup guide

Set up Yelp Fusion MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Yelp Fusion tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Yelp Fusion Analyst",
    goal="Access and analyze Yelp Fusion data via MCP.",
    backstory="Expert analyst with direct Yelp Fusion access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Yelp Fusion transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Yelp Fusion Analyst",
        goal="Access and analyze Yelp Fusion data via MCP.",
        backstory="Expert analyst with direct Yelp Fusion access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Yelp Fusion transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

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 CrewAI

Assign a 'Data Retrieval Agent' to use `get_business_details`. This agent pulls comprehensive information for any business ID. The result is then passed to another specialized agent for summarization.

You'll create a 'Search Team.' One agent runs `search_businesses` initially. The next agent iterates through the results, calling `get_reviews` on each one to compile a full report.

Yes. An 'Operations Agent' can use `get_jobs`, taking natural language input like 'plumbing.' It then uses the results to cross-reference against existing businesses via `match_business`.

A dedicated agent can run `search_events`. If that yields too many results, a 'Moderator Agent' can then call `get_featured_event` to narrow the focus and present a single best option.

This server handles business reviews and general operational details. Because this is user-generated content, you'll need your agents to respect usage boundaries when analyzing the data.

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